Assessment of the demand for and influencing factors of hitchhiking, paratransit and e-hailing services in rural South Africa
| dc.contributor.advisor | Vanderschuren, Marianne | |
| dc.contributor.author | Serumula, Dime Motlalepula | |
| dc.date.accessioned | 2026-07-22T09:47:23Z | |
| dc.date.available | 2026-07-22T09:47:23Z | |
| dc.date.issued | 2026 | |
| dc.date.updated | 2026-07-22T09:46:08Z | |
| dc.description.abstract | Introduction: This PhD thesis takes a user-centric approach to analyse the long-distance (i.e., town-to-town) travel demand of public travellers in rural South Africa (SA) and evaluate the statistical significances of the current and potential future factors that influence the decisions of choosing travel modes. Focus is on three mainstream public modes, namely, paratransit (generally referred to as minibus taxis in SA), hitchhiking and e-hailing. Minibus taxis (MBT) is leading the mainstream public transport industry with over 70,0% (STATSSA, 2021b). A few of the dissatisfactory factors related to MBT includes long-distances between taxi rank/route and home, security at the taxi rank and in the taxi, overloading, road safety, and service quality – for instance, long waiting times, bad attitude from drivers (STATSSA, 2021b). Hitchhiking is widely a travel mode of the past; the first global high trends recorded during the Great Depression, i.e., 1920-1939 (Laviolette, 2016; Reid, 2020; and Schlebecker, 1958). Additionally, it is commonly stigmatised as risky and dangerous (Evans, 2021; Morton, 2016; McGuire, 2017; Sodero and Scott, 2016; and Wacquaint 2001), despite the statistically significant, empirical evidence (Swartz, 2020). Whilst e-hailing services (such as Uber and Bolt) are beginning to lead the mainstream public mobility in higherincome countries, their services are lacking in rural Africa (African Union, 2019; Coutinho et al., 2020; DoT, 2018; Franco et al., 2020; Poru et al., 2020; and Sorensen et al., 2021). Instead, some of the travellers are seen requesting and sharing rides informally over social media, e.g., Facebook, WhatsApp, Twitter (Serumula and Vanderschuren, 2023, 2024). In context, both access and mobility constitute the fundamental policy and planning priorities of a sustainable community (United Nations, 2021). Centralised, diversified and specialised socio-economic opportunities are key pillars of sustainable communities (Meijers and van der Wouw, 2019). Such opportunities are lacking in the rural communities, particularly in the context of Africa or lower-income countries; therefore, the rural dwellers travel long distances to reach these socio-economic opportunities (Asher and Novosad, 2018; and Eckhardt et al., 2018), Noticeably, private car ownership or access is very low in the rural communities; they depend on public transport (Acheampong et al., 2020; Boateng et al., 2022; and Enoch et al., 2006) and under 35,0% of Sub-Saharan Africans have access to public transport (United Nations, 2021). Moreover, these public modes are widely characterised by questionable service quality – primarily based on attitude of operators, information, punctuality, safety, affordability, schedule and service frequency (Anburuvel et al., 2022; Githui et al., 2009; Hewage, 2015; Sörensen et al., 2021; and Serumula and Vanderschuren 2023, 2024). Because governments are severely failing to provide adequate, efficient and reliable public transport services; consequently, mainstream mobilty services are rendered by monopoly players, who are private and operate informally, i.e., they do not use business models, which include business registrations, full labour rights for employees, and tax payments (Casad´o et al., 2020; Chayya, 2018; DoT, 2020; Motta et al., 2013; National Taxi Lekgotla, 2020; Wilkinson, 2008; and World Bank, 2020). Furthermore, there is continuous intense, violent rivalry between these public services, and their silo operations are leading to multi-stage trips, which translate into travel discomfort and bad experiences for the travellers (Aghion et al., 2018; Aziz et al., 2018; Bauer and Kisielewski, 2021; Fobosi, 2019; Kumar et al., 2016; National Taxi Lekgotla, 2020; and Székely and Novotný, 2022). Three research questions are posed, which include their respective objectives. These are as follows: 1. What is the travel demand of rural long-distance public transport services in South Africa? a. Outline the status quo or demand baseline of the three key services, i.e., e-hailing, hitchhiking and paratransit. b. Contrast the daily travel patterns of the three key services. c. Compare the waiting times of the three key services. d. Examine the travel schedules of e-hailing services. e. Evaluate the origins and destinations of e-hailing services. f. Analyse the mode combinations of the hitchhikers. 2. Which factors influence the use of rural long-distance hitchhiking in South Africa? a. Evaluate the statistical significances of the following: - - - - - Demographic Factors, Socio-economic Factors, Activity Factors, Trip Factors, and Long-Term Decisions. 3. Which factors have the potential to influence the future use of rural public transport services in South Africa? a. b. c. d. e. f. g. Describe the indigenous hitchhiking signs in the South African context. Evaluate the relationship between private car ownership and travelling frequency. Outline the mode usage and travel time of long-distance public travellers. Identify the preferred future sources for travel scheduling. Classify the language use related to (public) mobility. Analyse the e-hailing and paratransit related challenges over social media. Determine the system technology use of the public travellers. Methodology: A sequential exploratory mixed methods approach is deployed, following scholars such as Ambrosino et al. (2003), Crewell and Clark (2017), Plano Clark (2019) and Tutty and Rothery (2011), to provide a robust exploration of user needs to analyse the travel demand and factors influencing their decisions of travel modes. A case study of Waterberg District Municipality in Limpopo Province (SA) was carried out, which comprises six rural towns, namely, Bela-Bela, Lephalale, Modimolle, Mokopane, Mookgophong, and Thabazimbi. These towns have diverse socio-economic characteristics that represent most, if not all, rural towns in South Africa and many others in the lower-income countries, particularly in the African context. Most commonly, a case study is chosen as the research design, especially when time and/or resources are limited, but there is a need to explore a subject in depth (Gerring, 2006; Ivankova et al., 2016; Sandelowski, 2010; and Yin, 2011). The scholars add that a case study can provide real-world, concrete, and contextual knowledge through an array of methodologies. The understanding of one case is essential for understanding other cases, in which the rare case will reveal the aspects that are usually taken for granted, yet they are important (Ivankova et al., 2016; and Yin, 2011). Moreover, a case study can be constructed with one or more spatial and temporal aspects, without compromising the statistical representativeness of the real-world context. Six distinct primary datasets were collected using the Six Step approach (by Ambrosino et al., 2003) for assessing the user needs related to the technological mobility system. As suggested by scholars like Barker (1980), Russell (2011), and Walshe et al. (2011); this approach was combined with the Process and Determinants of Mobility Decisions (PDMD) framework (by López and Wong, 2019) for collecting and analysing the influencing factors of the public travellers. These datasets are as follows: • Quantitative, random one-on-one interviews of the public travellers in the case study, which were governed by brief, structured questions of participants who, at least, have hitchhiking experience, no mental instability (based on observations), and are 18 years old or above (m=429), Mar-Apr 2022. • Mixed methods, full-day observations of the hitchhikers in each of the 6 rural towns in the case study, Mar-Apr 2022. • Ethnographic observation of paratransit services in all towns of interest (one full day in each town), Mar-Apr 2022. Netnography or Social Network Analysis (SNA) of the public ride requests from and to rural towns of interest (n=418), Jan 2019 - Mar 2020 and Oct 2021 – Jun 2022. • SNA of the public ride advertisements to and from towns of interest (o=93), Jan 2019 - Mar 2020 and Oct 2021 – Jun 2022. • Netnography of the public general comments that relate to hitchhiking and paratransit services of any of the towns of interest (p=151), Jan 2019 - Mar 2020 and Oct 2021 – Jun 2022. The main analytical method is descriptive statistics, which were conducted using Microsoft Excel (version 16.63.1), as the most available manual interface/application. Where hypotheses are concerned, Chi-squared or t-test was utilised to evaluate the statistical significances of influencing factors. All 20 hypotheses, including their assessment tools, are demonstrated in Table 1 where, in each instance, at 95% Confidence Level (CI), the hypothesis cannot be rejected if the p-value is less than 0,05 (Chi-square/t-test) or t-score is greater than t-critical (t-test). In addition, a geospatial mapping was done to map all the hitchhiking corridors, using an open-source Geographic Information Systems (GIS) software called Quantum GIS, QGIS-LTR (version 3.22). Results: A brief of the findings is provided as per the research requisition, and this is as follows. The first research question is, what is the travel demand of rural long-distance public transport services in rural South Africa? The demand baseline of public transport for long-distance trips in rural South Africa is as follows: - - - Paratransit or minibus taxis (MBT): 104 outbound trips (1980 passengers) between Mar-Apr 2022 Hitchhiking: 429 random one-on-one interviews (100% response rate) between Mar-Apr 2022 e-Hailing or social media hailing: 418 ride requests between Jan 2019–- Mar 2020 and Oct 2021 – Jun 2022 The findings demonstrate co-existence of and demand for paratransit, hitchhiking and e-hailing services across this study area. There is a statistically insignificant difference between hitchhiking and e-hailing (over social media, Facebook), and this is also true because the local communities consider these modes as one and the same thing. Six distinct primary datasets were collected using the Six Step approach (by Ambrosino et al., 2003) for assessing the user needs related to the technological mobility system. As suggested by scholars like Barker (1980), Russell (2011), and Walshe et al. (2011); this approach was combined with the Process and Determinants of Mobility Decisions (PDMD) framework (by López and Wong, 2019) for collecting and analysing the influencing factors of the public travellers. These datasets are as follows: • Quantitative, random one-on-one interviews of the public travellers in the case study, which were governed by brief, structured questions of participants who, at least, have hitchhiking experience, no mental instability (based on observations), and are 18 years old or above (m=429), Mar-Apr 2022. • Mixed methods, full-day observations of the hitchhikers in each of the 6 rural towns in the case study, Mar-Apr 2022. • Ethnographic observation of paratransit services in all towns of interest (one full day in each town), Mar-Apr 2022. Netnography or Social Network Analysis (SNA) of the public ride requests from and to rural towns of interest (n=418), Jan 2019 - Mar 2020 and Oct 2021 – Jun 2022. • SNA of the public ride advertisements to and from towns of interest (o=93), Jan 2019 - Mar 2020 and Oct 2021 – Jun 2022. • Netnography of the public general comments that relate to hitchhiking and paratransit services of any of the towns of interest (p=151), Jan 2019 - Mar 2020 and Oct 2021 – Jun 2022. The main analytical method is descriptive statistics, which were conducted using Microsoft Excel (version 16.63.1), as the most available manual interface/application. Where hypotheses are concerned, Chi-squared or t-test was utilised to evaluate the statistical significances of influencing factors. All 20 hypotheses, including their assessment tools, are demonstrated in Table 1 where, in each instance, at 95% Confidence Level (CI), the hypothesis cannot be rejected if the p-value is less than 0,05 (Chi-square/t-test) or t-score is greater than t-critical (t-test). In addition, a geospatial mapping was done to map all the hitchhiking corridors, using an open-source Geographic Information Systems (GIS) software called Quantum GIS, QGIS-LTR (version 3.22). Results: A brief of the findings is provided as per the research requisition, and this is as follows. The first research question is, what is the travel demand of rural long-distance public transport services in rural South Africa? The demand baseline of public transport for long-distance trips in rural South Africa is as follows: - - - Paratransit or minibus taxis (MBT): 104 outbound trips (1980 passengers) between Mar-Apr 2022 Hitchhiking: 429 random one-on-one interviews (100% response rate) between Mar-Apr 2022 e-Hailing or social media hailing: 418 ride requests between Jan 2019–- Mar 2020 and Oct 2021 – Jun 2022 The findings demonstrate co-existence of and demand for paratransit, hitchhiking and e-hailing services across this study area. There is a statistically insignificant difference between hitchhiking and e-hailing (over social media, Facebook), and this is also true because the local communities consider these modes as one and the same thing. with a low demand. Recent studies indicate that this complementary effect is important for exploring and establishing sustainable transport (Agyei et al., 2024; Hasselwander et al., 2022; Porru et al., 2020; and Stanstna and Vaishar, 2017). On the ground, unfortunately, the paratransit drivers/operators perceive it as an unfair competition because other competing services are not ‘regulated' (Chirume, 2017; Feni, 2015; Gill, 2018; Kamga et al., 2022; Litan and Rivlin, 2001; National Lekgotla, 2020; Nchofoung and Asongu, 2022; and Platinum Weekly Newspaper, 2020). The translation of this unfair competition was observed during the field data collection. The paratransit drivers and operators consistently disrupt the other services, particularly hitchhiking, through physical and verbal violence, including the passengers/hitchhikers. Hitchhiking is by far the most efficient mode; the findings show short waiting times outside (less than 20 minutes) and inside the vehicle (less than 5 minutes). Interestingly, the disruption of hitchhiking by MBT drivers/operators is also positively contributing to the short waiting times, particularly inside the hitched rides. These findings correspond with the idea that multiple modes triggers competition, as long intended by international commissions on public transport (Fobosi, 2019; and Kumar et al., 2016), which will then lead to improved service quality (for instance Hasselwander et al., 2022). The latter is clear because e-hailing offers prior travelling arrangements (covering more than 50% of the traveller need/preference) and on-the-go or instant travelling. For paratransit, the waiting times inside the vehicles are generally long (i.e., an hour or more) but short (less than 5 minutes) outside of the vehicle. Nevertheless, the public travellers hold the perceptions that the paratransit has long waiting times (either outside or inside of the vehicle). In addition to the waiting times, the findings revealed the demand per hour for the e-hailing service, where a day (24-hours cycle) was divided into four time periods, namely 00:00–- 06:00, 06:00–-12:00, 12:00–-18:00, and 18:00–-00:00. The results demonstrate that the highest rates are during the period 06:00–12:00 (63,3 requests/hour), followed by 12:00–18:00 (46,3 requests/hour), 18:00–00:00 (32,17 requests/hour) and then, 00:00–06:00 (4,83 requests/hour). That is, the peak time is during the day (06:00–18:00) and off-peak is at night (18:00–06:00). Many public travellers start their e-hailing trips from rural towns with high human populations (i.e., Mokopane). However, the relationship is not clear for moderate and low populated towns. Destinations, on the other hand, are aligning with what is noted in literature (for example Li et al., 2019; Tacoli et al., 2015), that is, humans are inevitably transforming with the aim to acquire urban-industrial and knowledge economies, as well as an improved quality of life. Common destinations, including for the travellers outside of the case study area, are those that have recently witnessed drastic economic growth, namely Lephalale (renewable energy generation, ICT hubs), and Mokopane (new mining operations). Additional common destinations are rural towns with established economic aspects, namely, Bela-Bela (tourism) and Modimolle (regional government departments). Strikingly, as categorised by Gonzalez-Gonzalez and Nogues (2019), a negative or pump effect (subtraction of human resources) is observed for females, i.e., their destinations are mainly outside the case study area (generally to urban areas), but a positive or tunnel effect (addition or complimentary of human capital) for their counterparts. This finding can be linked to the general argument that economic spaces should also be setup in ways to accommodate the female groups, as much as they accommodate males (Heidari et al., 2016; and Legovini et al., 2023). As many as 86,0% of the mode combinations can be interpreted with acceptable statistical significance (at 95% confidence level). These combinations are categorised into Choice A (52,5%) and Choice B (33,5%). Both Choice A and B are characterised by walking and paratransit/MBT in the first and last mile. Precisely, Choice A has walking in the first and last mile, and MBT only in the first mile (therefore, 3 transfers and 4 stages), whilst Choice B has both modes in the first and last mile (making 4 transfers and 5 stages). This finding serves as the base for accepting both Hypothesis 3 (H3) and 4 (H4), where H3 states that the travel behaviour between female and male public travellers has insignificant differences, and H4 states the most common modal choice in the last mile of hitchhiking does not leads to the shortest, average travel time. An additional element for distinguishing Choices A and B is the average travel distance for the main stage, i.e., 126,9 kilometres (standard deviation – 41,5), and 141,5 kilometres (standard deviation – 29,6), respectively. In essence, many of the rural public travellers take the mode combination with fewer multi-stage trips and a lower average travel time. This evidence supports the argument against multi-stage trips, because the transfers are likely to generate discomfort, disrupt travel experience, reduce service competitiveness, and create travel penalties on the traveller (Bauer and Kisielewski, 2021; and Székely and Novotný, 2022). Apart from Choices A and B, there are 12 other modal combinations, but none of them were noted, at least by 50 public travellers, to qualify them to be examined with acceptable statistical significance (Chi square). Interestingly, these 12 choices are the only ones including private cars. This finding supports the literature that rural communities widely depend on public transport (Agyemang, 2020; Dzisi et al., 2020; Grahn et al., 2019; Lavieri and Bhat 2019; Lewis and MacKenzie, 2017; and Schaller, 2018) and the private car ownerships/access are low (Acheampong et al., 2020; Boateng et al., 2022; and Enoch et al., 2006). The second research question is, what influences the use of rural long-distance hitchhiking in South Africa? Five categories of factors are revealed. (a) Demographic Factors (total: 6) – age, gender, marital status, household size, dependents, mobility impairments (or disability), out of which, only age and mobility impairments are statistically significant in influencing the decisions of modal choices for the long-distance of public travellers in rural South Africa. (b) Socio-Economic Factors (total: 9) – private car access, travel mate, hitchhiking experience, known hitchhiker, hitchhiking dissatisfaction, overloading experience, overloading related emotions, employment status, accessible car priority. Only three factors are found to have insignificant influence on the modal choices, and these are hitchhiking, dissatisfaction, employment status, and accessible car priority. (c) Activity Factors (total: 5) – hitchhiking assistance, hitchhiking disruption – related motions, hitched ride travel speed, payment time, travel fare query. All the above-mentioned factors have a statistically significant relationship with the modal choices. (d) Trip Factors (total: 6) – trip decision, destination type, trip distance, return trip, trip frequency and travel schedule. All listed factors are statistically significant in the decisions of choosing modes. (e) Long-Term Decisions (total: 5) – hitched ride incidents, paratransit incidents, own car incidents, family car incidents, and satisfactory factors – all these factors are statistically significant in the decisions of choosing modes. The third and last research question is, which factors have the potential to influence the future use of public transport services in South Africa? (a) Four types of hand signs are noted, which are arguably unique to South African public transport services, namely, (i) a static hand sign with an arm slightly raised and a protruding index finger along the direction of the road, (ii) a dynamic hand sign, where an arm is parallel to the ground and a hand is open and moved inwards and outwards to point to the desired road turn/route network, (iii) the relaxed hand position whilst swinging an open hand in the direction of the destination and back, and (iv) another dynamic hand sign, where an arm is parallel to the ground in the direction of the road and the wrist is flipped inwards and outwards to portray a road network with multiple turns. (b) Private car ownership highlights the potential to reduces the travel frequencies, however, additional statistical significant sample size (future research) is needed to confirm this finding. (c) Many public travellers indicate a variety of options to arrange their travelling. Of these, the most preferred option is a phone call, followed by an in-person interaction, a combination of inperson, phone call, and phone applications, as well as, family members. All these preferences are statistically significant. (d) Two language structures are used to arrange public modes, namely, ride request (by the travellers) and ride advertisement (by ride drivers/operators). These structures remain the same regardless of the diverse language uses, ranging from English through native languages to slang. Furthermore, ride request structure is the most prominent one used. (e) A total of 8topics on social media are yielded regarding public modes, that is, road safety (most engaged), followed by service quality, economic, access, information quality, customer misbehaviour, system quality, and then, equity. The associated posts are mainly from female timelines, followed by male timelines as well as, page/groups timelines. But most engaged posts are for pages, followed by males, and then, females. Moreover, weekly trends show between 10,0-20,0% of the posts are generated daily, where the highest peak day is Thursday and the lowest is Friday. (f) Rural public travellers generally use 4 system technologies , that is, smartphone applications, cell phone banking, banking cards, and shopping cards. Only less than 16,0% of both female and male travellers do not use these technologies. Usages are mostly on a daily, weekly, bi-weekly, and then, occasional basis. Conclusion: Unlike paratransit (minibus taxis, MBT), e-hailing services and hitchhiking services are widely overlooked (in Transport Planning and Management). Yet their demand, in rural South African communities, is significant, highlighting the essential roles in enabling and expanding access to socio-economic opportunities at relevant travel costs for travellers with diverse needs and challenges. These services are mainstream public modes across all six rural towns, particularly for long-distance public travellers. Although there is continuous, violent competition for the markets, these three modes are complementing rather than substituting each other. Early hours (before 7am) mark the peak period of e-hailing services, followed by hitchhiking peak (until 10am) and then, from noon until late afternoon marks the peak period for MBT. Notably, e-hailing services run until late evening (11pm). Moreover, e-hailing services are fundamentally occurring over social media (commonly, Facebook) instead of customised applications such as Uber, Bolts and others like DiDI and inDrive. Statistically, there is an insignificant variation between hitchhiking and e-hailing (over social media), and this is not surprising because the locals (i.e., South Africans) perceive the two services as one and the same thing. Hitchhiking is mainly a habit (i.e., many travellers have over 6 months experience), and the Covid-19 pandemic had a statistically insignificant impact on these services. Origin-destination shows the importance of land use improvements (e.g., new mining operations) on transport or societal movements, and further highlights the gender disparities. Over 80,0% of statistically significant mode combinations for long-distance trips (hitchhiking) only comprise walking and paratransit in the first and last miles. These mode combinations are distinguished by relatively fewer transfers and stages, which are evident in the last mile, i.e., one transfer and one stage less. Many public travellers prefer trips with fewer transfers and stages, namely, Choice A – walk and paratransit (first mile), then main stage (hitchhiking), and then, walking (last mile) – which results in 3 transfers and 4 stages. The other alternative combination, for relatively fewer travellers, is Choice B, which is characterised by 4 transfers and 5 stages, i.e., walking and MBT (first mile), and then, MBT and walking (last mile). Due to limited access or ownerships of private cars, all other mode combinations that comprise private cars are statistically insignificant. There is an array of factors (namely, demographic, socio-economic, activity, long-term decisions and trip) that influence these mode combinations. A few interesting factors, which are not statistically significant to influence the decisions for choosing modes, include gender, employment status, hitchhiking dissatisfaction, and personal/road safety of hitchhiking and paratransit. Remarkably, the long-term decisions associated with personal/road safety of hitchhiking are insignificant; instead, many are based on hearsay and/or rumours. Influencing factors suggest that future travel behaviours are likely to change, as human society is continuously transforming to seek better livelihoods, particularly urban-industrial and knowledge economies. Evidence lies in the uncommon usage of (existing) customised e-hailing services in rural South Africa. On the other hand, rural communities widely reveal high penetration of system technologies not limited to (public) transport services (for instance, shopping cards/vouchers). In addition, the lack of integrated public transport services and the violent competition therein, triggers the public travellers (for instances, hitchhikers) to constantly seek roadside hand signs to avoid disruptions by the MBT operators, and/or clearly and quickly request bypassing rides. There must be thorough additional socio-orientated studies to succinctly provide scenarios that will help maximise the societal benefits of mobility and/or expand the accessibility of (rural) South Africans to reach the desired socio-economic opportunities at efficient travel costs, where fair and professional multi-modal competition is promoted, service quality is encouraged, and the traveller-ride matching is seamless and efficient across trip transfers or stages. Reflection and recommendations: There are opportunities to explore and invest in transfer and intermodal transit systems in rural areas of lower-income countries. Although the travellers of all gender and age groups walk somewhere during their trips, the roads from their origins (i.e., the case study) are very poor, and cycling or driving during rainy days, for instance, will be almost impossible, as many become very muddy. Hitchhiking is by far the best alternative public transit service for long-distance trips in rural areas of lower-income countries, and it has developed characteristics of being completely irrational and dangerous, yet there is no statistical significance or empirical evidence to support this. In addition, there are different market niches for hitchhiking and paratransit. Nevertheless, the paratransit drivers/operators take measures that are arguably violent in the face of service competition. Essentially, competition needs to be broadly promoted instead of being seen as a (business) threat in the public transport industry. Furthermore, changing the status, i.e., from ‘free market' to formal, of the hitchhiking services could help mitigate some of the user challenges (e.g., disruption, lack of safety), and increase the government benefits (i.e., through road and/or parking pricing initiatives of the hitched rides) as well as the general economic growth of the area (i.e., attracting more operators and users, who might be avoiding the service, due to lack of specific policy coverage). Whilst on economic growth, seeking ways to formalise hitchhiking could help increase the labour force. Although traditional hitchhiking is still taking place, some of the public travellers have revolutionised hitchhiking, i.e., they are hitchhiking on social media. Unlike traditional hailing (i.e., on the roadside), social media thumbing is relatively safer, as the ride drivers are known. That is, tracking down such a driver is much easier and, if one attempts to do unsafe acts (as a driver), a profile of unsafe ride drivers (and other challenges) can be ‘dented' to alert future potential victims, e.g., through word-of-mouth, posts on the platforms, and some form of rating system. At present, the platform that entails the utilised datasets (i.e., Facebook), does not have the specific option to rate a profile/person, based on the service they provide, outside of what is called ‘marketplace' (i.e., interface specifically for advertising). Remarkably, this study is one of the guidelines for directing the development of ‘smart' (widely referring to sustainable) rural; the scholars state that the adoption and use of information technology has the potential to improve the economic welfare of rural areas. This approach is increasingly categorised as Mobility-as-a-Service (MaaS) or -Feature (MaaF). Precisely, for this case study, the rural root-causes of hitchhiking disruption need to be examined, include the perspective of MBT drivers/operators and the general rural communities (for instance, business, community organisations). In addition, the cost-benefit analyses will be necessary to guide the relevant and adequate adoption of public transport services that cater for the needs of diverse public travellers in the same areas. This process will further extend to other socio-economic dimensions (for example pathways, interchanges) that will reduce and mitigate social exclusions, spatial or territorial disparities and general hindrances to access socio-economic opportunities. In that light, the restructuring of policies, legislation, and strategies that governs mainstream institutions will be inevitable. Sadly, the institutions in lower-income countries are currently lacking the strategies to guide efficient and relevant tech-orientated transit services to all relevant key stakeholders. Notably, land use and transport play a crucial role in rural cohesion and revitalisation and, in part, the broad readdressing of gender disparity. rural cohesion and revitalisation comprise positive effects from improved socio-economic and land use or built environment, such as reduction in social exclusion and spatial/territorial disparities, increased access to social, economic, health and other livelihood opportunities. The net effect of female origin-destination patterns indicates migration from rural to urban areas, and rural-rural and urban-rural for their counterparts. Policy Implication: The best and urgently needed solution would be to develop and maintain investments ranging from as cheap as walking paths in these areas - which will be a lot more cost effective, unlike sourcing and maintenance of bicycles and making other forms of subsidies (e.g., feeder vehicles) – through to as expensive as the integration of Information and Communication Technology (like in Uber, Bolt – i.e., a single seamless digital platform (MaaS/MaaF). The above have direct impacts, at least, on the infrastructure development and transport planning and the relationships thereof, where polices are developed and implemented to inspire the development and maintenance of the stakeholder partnerships and entrepreneurial spirit in the whole public mobility supply chain, which also comprises inclusive strategies to attract the female population, who are currently in urban areas, to be more ‘attractive'. | |
| dc.identifier.apacitation | Serumula, D. M. (2026). <i>Assessment of the demand for and influencing factors of hitchhiking, paratransit and e-hailing services in rural South Africa</i>. (). University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Civil Engineering. Retrieved from http://hdl.handle.net/11427/43630 | en_ZA |
| dc.identifier.chicagocitation | Serumula, Dime Motlalepula. <i>"Assessment of the demand for and influencing factors of hitchhiking, paratransit and e-hailing services in rural South Africa."</i> ., University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Civil Engineering, 2026. http://hdl.handle.net/11427/43630 | en_ZA |
| dc.identifier.citation | Serumula, D.M. 2026. Assessment of the demand for and influencing factors of hitchhiking, paratransit and e-hailing services in rural South Africa. . University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Civil Engineering. http://hdl.handle.net/11427/43630 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Serumula, Dime Motlalepula AB - Introduction: This PhD thesis takes a user-centric approach to analyse the long-distance (i.e., town-to-town) travel demand of public travellers in rural South Africa (SA) and evaluate the statistical significances of the current and potential future factors that influence the decisions of choosing travel modes. Focus is on three mainstream public modes, namely, paratransit (generally referred to as minibus taxis in SA), hitchhiking and e-hailing. Minibus taxis (MBT) is leading the mainstream public transport industry with over 70,0% (STATSSA, 2021b). A few of the dissatisfactory factors related to MBT includes long-distances between taxi rank/route and home, security at the taxi rank and in the taxi, overloading, road safety, and service quality – for instance, long waiting times, bad attitude from drivers (STATSSA, 2021b). Hitchhiking is widely a travel mode of the past; the first global high trends recorded during the Great Depression, i.e., 1920-1939 (Laviolette, 2016; Reid, 2020; and Schlebecker, 1958). Additionally, it is commonly stigmatised as risky and dangerous (Evans, 2021; Morton, 2016; McGuire, 2017; Sodero and Scott, 2016; and Wacquaint 2001), despite the statistically significant, empirical evidence (Swartz, 2020). Whilst e-hailing services (such as Uber and Bolt) are beginning to lead the mainstream public mobility in higherincome countries, their services are lacking in rural Africa (African Union, 2019; Coutinho et al., 2020; DoT, 2018; Franco et al., 2020; Poru et al., 2020; and Sorensen et al., 2021). Instead, some of the travellers are seen requesting and sharing rides informally over social media, e.g., Facebook, WhatsApp, Twitter (Serumula and Vanderschuren, 2023, 2024). In context, both access and mobility constitute the fundamental policy and planning priorities of a sustainable community (United Nations, 2021). Centralised, diversified and specialised socio-economic opportunities are key pillars of sustainable communities (Meijers and van der Wouw, 2019). Such opportunities are lacking in the rural communities, particularly in the context of Africa or lower-income countries; therefore, the rural dwellers travel long distances to reach these socio-economic opportunities (Asher and Novosad, 2018; and Eckhardt et al., 2018), Noticeably, private car ownership or access is very low in the rural communities; they depend on public transport (Acheampong et al., 2020; Boateng et al., 2022; and Enoch et al., 2006) and under 35,0% of Sub-Saharan Africans have access to public transport (United Nations, 2021). Moreover, these public modes are widely characterised by questionable service quality – primarily based on attitude of operators, information, punctuality, safety, affordability, schedule and service frequency (Anburuvel et al., 2022; Githui et al., 2009; Hewage, 2015; Sörensen et al., 2021; and Serumula and Vanderschuren 2023, 2024). Because governments are severely failing to provide adequate, efficient and reliable public transport services; consequently, mainstream mobilty services are rendered by monopoly players, who are private and operate informally, i.e., they do not use business models, which include business registrations, full labour rights for employees, and tax payments (Casad´o et al., 2020; Chayya, 2018; DoT, 2020; Motta et al., 2013; National Taxi Lekgotla, 2020; Wilkinson, 2008; and World Bank, 2020). Furthermore, there is continuous intense, violent rivalry between these public services, and their silo operations are leading to multi-stage trips, which translate into travel discomfort and bad experiences for the travellers (Aghion et al., 2018; Aziz et al., 2018; Bauer and Kisielewski, 2021; Fobosi, 2019; Kumar et al., 2016; National Taxi Lekgotla, 2020; and Székely and Novotný, 2022). Three research questions are posed, which include their respective objectives. These are as follows: 1. What is the travel demand of rural long-distance public transport services in South Africa? a. Outline the status quo or demand baseline of the three key services, i.e., e-hailing, hitchhiking and paratransit. b. Contrast the daily travel patterns of the three key services. c. Compare the waiting times of the three key services. d. Examine the travel schedules of e-hailing services. e. Evaluate the origins and destinations of e-hailing services. f. Analyse the mode combinations of the hitchhikers. 2. Which factors influence the use of rural long-distance hitchhiking in South Africa? a. Evaluate the statistical significances of the following: - - - - - Demographic Factors, Socio-economic Factors, Activity Factors, Trip Factors, and Long-Term Decisions. 3. Which factors have the potential to influence the future use of rural public transport services in South Africa? a. b. c. d. e. f. g. Describe the indigenous hitchhiking signs in the South African context. Evaluate the relationship between private car ownership and travelling frequency. Outline the mode usage and travel time of long-distance public travellers. Identify the preferred future sources for travel scheduling. Classify the language use related to (public) mobility. Analyse the e-hailing and paratransit related challenges over social media. Determine the system technology use of the public travellers. Methodology: A sequential exploratory mixed methods approach is deployed, following scholars such as Ambrosino et al. (2003), Crewell and Clark (2017), Plano Clark (2019) and Tutty and Rothery (2011), to provide a robust exploration of user needs to analyse the travel demand and factors influencing their decisions of travel modes. A case study of Waterberg District Municipality in Limpopo Province (SA) was carried out, which comprises six rural towns, namely, Bela-Bela, Lephalale, Modimolle, Mokopane, Mookgophong, and Thabazimbi. These towns have diverse socio-economic characteristics that represent most, if not all, rural towns in South Africa and many others in the lower-income countries, particularly in the African context. Most commonly, a case study is chosen as the research design, especially when time and/or resources are limited, but there is a need to explore a subject in depth (Gerring, 2006; Ivankova et al., 2016; Sandelowski, 2010; and Yin, 2011). The scholars add that a case study can provide real-world, concrete, and contextual knowledge through an array of methodologies. The understanding of one case is essential for understanding other cases, in which the rare case will reveal the aspects that are usually taken for granted, yet they are important (Ivankova et al., 2016; and Yin, 2011). Moreover, a case study can be constructed with one or more spatial and temporal aspects, without compromising the statistical representativeness of the real-world context. Six distinct primary datasets were collected using the Six Step approach (by Ambrosino et al., 2003) for assessing the user needs related to the technological mobility system. As suggested by scholars like Barker (1980), Russell (2011), and Walshe et al. (2011); this approach was combined with the Process and Determinants of Mobility Decisions (PDMD) framework (by López and Wong, 2019) for collecting and analysing the influencing factors of the public travellers. These datasets are as follows: • Quantitative, random one-on-one interviews of the public travellers in the case study, which were governed by brief, structured questions of participants who, at least, have hitchhiking experience, no mental instability (based on observations), and are 18 years old or above (m=429), Mar-Apr 2022. • Mixed methods, full-day observations of the hitchhikers in each of the 6 rural towns in the case study, Mar-Apr 2022. • Ethnographic observation of paratransit services in all towns of interest (one full day in each town), Mar-Apr 2022. Netnography or Social Network Analysis (SNA) of the public ride requests from and to rural towns of interest (n=418), Jan 2019 - Mar 2020 and Oct 2021 – Jun 2022. • SNA of the public ride advertisements to and from towns of interest (o=93), Jan 2019 - Mar 2020 and Oct 2021 – Jun 2022. • Netnography of the public general comments that relate to hitchhiking and paratransit services of any of the towns of interest (p=151), Jan 2019 - Mar 2020 and Oct 2021 – Jun 2022. The main analytical method is descriptive statistics, which were conducted using Microsoft Excel (version 16.63.1), as the most available manual interface/application. Where hypotheses are concerned, Chi-squared or t-test was utilised to evaluate the statistical significances of influencing factors. All 20 hypotheses, including their assessment tools, are demonstrated in Table 1 where, in each instance, at 95% Confidence Level (CI), the hypothesis cannot be rejected if the p-value is less than 0,05 (Chi-square/t-test) or t-score is greater than t-critical (t-test). In addition, a geospatial mapping was done to map all the hitchhiking corridors, using an open-source Geographic Information Systems (GIS) software called Quantum GIS, QGIS-LTR (version 3.22). Results: A brief of the findings is provided as per the research requisition, and this is as follows. The first research question is, what is the travel demand of rural long-distance public transport services in rural South Africa? The demand baseline of public transport for long-distance trips in rural South Africa is as follows: - - - Paratransit or minibus taxis (MBT): 104 outbound trips (1980 passengers) between Mar-Apr 2022 Hitchhiking: 429 random one-on-one interviews (100% response rate) between Mar-Apr 2022 e-Hailing or social media hailing: 418 ride requests between Jan 2019–- Mar 2020 and Oct 2021 – Jun 2022 The findings demonstrate co-existence of and demand for paratransit, hitchhiking and e-hailing services across this study area. There is a statistically insignificant difference between hitchhiking and e-hailing (over social media, Facebook), and this is also true because the local communities consider these modes as one and the same thing. Six distinct primary datasets were collected using the Six Step approach (by Ambrosino et al., 2003) for assessing the user needs related to the technological mobility system. As suggested by scholars like Barker (1980), Russell (2011), and Walshe et al. (2011); this approach was combined with the Process and Determinants of Mobility Decisions (PDMD) framework (by López and Wong, 2019) for collecting and analysing the influencing factors of the public travellers. These datasets are as follows: • Quantitative, random one-on-one interviews of the public travellers in the case study, which were governed by brief, structured questions of participants who, at least, have hitchhiking experience, no mental instability (based on observations), and are 18 years old or above (m=429), Mar-Apr 2022. • Mixed methods, full-day observations of the hitchhikers in each of the 6 rural towns in the case study, Mar-Apr 2022. • Ethnographic observation of paratransit services in all towns of interest (one full day in each town), Mar-Apr 2022. Netnography or Social Network Analysis (SNA) of the public ride requests from and to rural towns of interest (n=418), Jan 2019 - Mar 2020 and Oct 2021 – Jun 2022. • SNA of the public ride advertisements to and from towns of interest (o=93), Jan 2019 - Mar 2020 and Oct 2021 – Jun 2022. • Netnography of the public general comments that relate to hitchhiking and paratransit services of any of the towns of interest (p=151), Jan 2019 - Mar 2020 and Oct 2021 – Jun 2022. The main analytical method is descriptive statistics, which were conducted using Microsoft Excel (version 16.63.1), as the most available manual interface/application. Where hypotheses are concerned, Chi-squared or t-test was utilised to evaluate the statistical significances of influencing factors. All 20 hypotheses, including their assessment tools, are demonstrated in Table 1 where, in each instance, at 95% Confidence Level (CI), the hypothesis cannot be rejected if the p-value is less than 0,05 (Chi-square/t-test) or t-score is greater than t-critical (t-test). In addition, a geospatial mapping was done to map all the hitchhiking corridors, using an open-source Geographic Information Systems (GIS) software called Quantum GIS, QGIS-LTR (version 3.22). Results: A brief of the findings is provided as per the research requisition, and this is as follows. The first research question is, what is the travel demand of rural long-distance public transport services in rural South Africa? The demand baseline of public transport for long-distance trips in rural South Africa is as follows: - - - Paratransit or minibus taxis (MBT): 104 outbound trips (1980 passengers) between Mar-Apr 2022 Hitchhiking: 429 random one-on-one interviews (100% response rate) between Mar-Apr 2022 e-Hailing or social media hailing: 418 ride requests between Jan 2019–- Mar 2020 and Oct 2021 – Jun 2022 The findings demonstrate co-existence of and demand for paratransit, hitchhiking and e-hailing services across this study area. There is a statistically insignificant difference between hitchhiking and e-hailing (over social media, Facebook), and this is also true because the local communities consider these modes as one and the same thing. with a low demand. Recent studies indicate that this complementary effect is important for exploring and establishing sustainable transport (Agyei et al., 2024; Hasselwander et al., 2022; Porru et al., 2020; and Stanstna and Vaishar, 2017). On the ground, unfortunately, the paratransit drivers/operators perceive it as an unfair competition because other competing services are not ‘regulated' (Chirume, 2017; Feni, 2015; Gill, 2018; Kamga et al., 2022; Litan and Rivlin, 2001; National Lekgotla, 2020; Nchofoung and Asongu, 2022; and Platinum Weekly Newspaper, 2020). The translation of this unfair competition was observed during the field data collection. The paratransit drivers and operators consistently disrupt the other services, particularly hitchhiking, through physical and verbal violence, including the passengers/hitchhikers. Hitchhiking is by far the most efficient mode; the findings show short waiting times outside (less than 20 minutes) and inside the vehicle (less than 5 minutes). Interestingly, the disruption of hitchhiking by MBT drivers/operators is also positively contributing to the short waiting times, particularly inside the hitched rides. These findings correspond with the idea that multiple modes triggers competition, as long intended by international commissions on public transport (Fobosi, 2019; and Kumar et al., 2016), which will then lead to improved service quality (for instance Hasselwander et al., 2022). The latter is clear because e-hailing offers prior travelling arrangements (covering more than 50% of the traveller need/preference) and on-the-go or instant travelling. For paratransit, the waiting times inside the vehicles are generally long (i.e., an hour or more) but short (less than 5 minutes) outside of the vehicle. Nevertheless, the public travellers hold the perceptions that the paratransit has long waiting times (either outside or inside of the vehicle). In addition to the waiting times, the findings revealed the demand per hour for the e-hailing service, where a day (24-hours cycle) was divided into four time periods, namely 00:00–- 06:00, 06:00–-12:00, 12:00–-18:00, and 18:00–-00:00. The results demonstrate that the highest rates are during the period 06:00–12:00 (63,3 requests/hour), followed by 12:00–18:00 (46,3 requests/hour), 18:00–00:00 (32,17 requests/hour) and then, 00:00–06:00 (4,83 requests/hour). That is, the peak time is during the day (06:00–18:00) and off-peak is at night (18:00–06:00). Many public travellers start their e-hailing trips from rural towns with high human populations (i.e., Mokopane). However, the relationship is not clear for moderate and low populated towns. Destinations, on the other hand, are aligning with what is noted in literature (for example Li et al., 2019; Tacoli et al., 2015), that is, humans are inevitably transforming with the aim to acquire urban-industrial and knowledge economies, as well as an improved quality of life. Common destinations, including for the travellers outside of the case study area, are those that have recently witnessed drastic economic growth, namely Lephalale (renewable energy generation, ICT hubs), and Mokopane (new mining operations). Additional common destinations are rural towns with established economic aspects, namely, Bela-Bela (tourism) and Modimolle (regional government departments). Strikingly, as categorised by Gonzalez-Gonzalez and Nogues (2019), a negative or pump effect (subtraction of human resources) is observed for females, i.e., their destinations are mainly outside the case study area (generally to urban areas), but a positive or tunnel effect (addition or complimentary of human capital) for their counterparts. This finding can be linked to the general argument that economic spaces should also be setup in ways to accommodate the female groups, as much as they accommodate males (Heidari et al., 2016; and Legovini et al., 2023). As many as 86,0% of the mode combinations can be interpreted with acceptable statistical significance (at 95% confidence level). These combinations are categorised into Choice A (52,5%) and Choice B (33,5%). Both Choice A and B are characterised by walking and paratransit/MBT in the first and last mile. Precisely, Choice A has walking in the first and last mile, and MBT only in the first mile (therefore, 3 transfers and 4 stages), whilst Choice B has both modes in the first and last mile (making 4 transfers and 5 stages). This finding serves as the base for accepting both Hypothesis 3 (H3) and 4 (H4), where H3 states that the travel behaviour between female and male public travellers has insignificant differences, and H4 states the most common modal choice in the last mile of hitchhiking does not leads to the shortest, average travel time. An additional element for distinguishing Choices A and B is the average travel distance for the main stage, i.e., 126,9 kilometres (standard deviation – 41,5), and 141,5 kilometres (standard deviation – 29,6), respectively. In essence, many of the rural public travellers take the mode combination with fewer multi-stage trips and a lower average travel time. This evidence supports the argument against multi-stage trips, because the transfers are likely to generate discomfort, disrupt travel experience, reduce service competitiveness, and create travel penalties on the traveller (Bauer and Kisielewski, 2021; and Székely and Novotný, 2022). Apart from Choices A and B, there are 12 other modal combinations, but none of them were noted, at least by 50 public travellers, to qualify them to be examined with acceptable statistical significance (Chi square). Interestingly, these 12 choices are the only ones including private cars. This finding supports the literature that rural communities widely depend on public transport (Agyemang, 2020; Dzisi et al., 2020; Grahn et al., 2019; Lavieri and Bhat 2019; Lewis and MacKenzie, 2017; and Schaller, 2018) and the private car ownerships/access are low (Acheampong et al., 2020; Boateng et al., 2022; and Enoch et al., 2006). The second research question is, what influences the use of rural long-distance hitchhiking in South Africa? Five categories of factors are revealed. (a) Demographic Factors (total: 6) – age, gender, marital status, household size, dependents, mobility impairments (or disability), out of which, only age and mobility impairments are statistically significant in influencing the decisions of modal choices for the long-distance of public travellers in rural South Africa. (b) Socio-Economic Factors (total: 9) – private car access, travel mate, hitchhiking experience, known hitchhiker, hitchhiking dissatisfaction, overloading experience, overloading related emotions, employment status, accessible car priority. Only three factors are found to have insignificant influence on the modal choices, and these are hitchhiking, dissatisfaction, employment status, and accessible car priority. (c) Activity Factors (total: 5) – hitchhiking assistance, hitchhiking disruption – related motions, hitched ride travel speed, payment time, travel fare query. All the above-mentioned factors have a statistically significant relationship with the modal choices. (d) Trip Factors (total: 6) – trip decision, destination type, trip distance, return trip, trip frequency and travel schedule. All listed factors are statistically significant in the decisions of choosing modes. (e) Long-Term Decisions (total: 5) – hitched ride incidents, paratransit incidents, own car incidents, family car incidents, and satisfactory factors – all these factors are statistically significant in the decisions of choosing modes. The third and last research question is, which factors have the potential to influence the future use of public transport services in South Africa? (a) Four types of hand signs are noted, which are arguably unique to South African public transport services, namely, (i) a static hand sign with an arm slightly raised and a protruding index finger along the direction of the road, (ii) a dynamic hand sign, where an arm is parallel to the ground and a hand is open and moved inwards and outwards to point to the desired road turn/route network, (iii) the relaxed hand position whilst swinging an open hand in the direction of the destination and back, and (iv) another dynamic hand sign, where an arm is parallel to the ground in the direction of the road and the wrist is flipped inwards and outwards to portray a road network with multiple turns. (b) Private car ownership highlights the potential to reduces the travel frequencies, however, additional statistical significant sample size (future research) is needed to confirm this finding. (c) Many public travellers indicate a variety of options to arrange their travelling. Of these, the most preferred option is a phone call, followed by an in-person interaction, a combination of inperson, phone call, and phone applications, as well as, family members. All these preferences are statistically significant. (d) Two language structures are used to arrange public modes, namely, ride request (by the travellers) and ride advertisement (by ride drivers/operators). These structures remain the same regardless of the diverse language uses, ranging from English through native languages to slang. Furthermore, ride request structure is the most prominent one used. (e) A total of 8topics on social media are yielded regarding public modes, that is, road safety (most engaged), followed by service quality, economic, access, information quality, customer misbehaviour, system quality, and then, equity. The associated posts are mainly from female timelines, followed by male timelines as well as, page/groups timelines. But most engaged posts are for pages, followed by males, and then, females. Moreover, weekly trends show between 10,0-20,0% of the posts are generated daily, where the highest peak day is Thursday and the lowest is Friday. (f) Rural public travellers generally use 4 system technologies , that is, smartphone applications, cell phone banking, banking cards, and shopping cards. Only less than 16,0% of both female and male travellers do not use these technologies. Usages are mostly on a daily, weekly, bi-weekly, and then, occasional basis. Conclusion: Unlike paratransit (minibus taxis, MBT), e-hailing services and hitchhiking services are widely overlooked (in Transport Planning and Management). Yet their demand, in rural South African communities, is significant, highlighting the essential roles in enabling and expanding access to socio-economic opportunities at relevant travel costs for travellers with diverse needs and challenges. These services are mainstream public modes across all six rural towns, particularly for long-distance public travellers. Although there is continuous, violent competition for the markets, these three modes are complementing rather than substituting each other. Early hours (before 7am) mark the peak period of e-hailing services, followed by hitchhiking peak (until 10am) and then, from noon until late afternoon marks the peak period for MBT. Notably, e-hailing services run until late evening (11pm). Moreover, e-hailing services are fundamentally occurring over social media (commonly, Facebook) instead of customised applications such as Uber, Bolts and others like DiDI and inDrive. Statistically, there is an insignificant variation between hitchhiking and e-hailing (over social media), and this is not surprising because the locals (i.e., South Africans) perceive the two services as one and the same thing. Hitchhiking is mainly a habit (i.e., many travellers have over 6 months experience), and the Covid-19 pandemic had a statistically insignificant impact on these services. Origin-destination shows the importance of land use improvements (e.g., new mining operations) on transport or societal movements, and further highlights the gender disparities. Over 80,0% of statistically significant mode combinations for long-distance trips (hitchhiking) only comprise walking and paratransit in the first and last miles. These mode combinations are distinguished by relatively fewer transfers and stages, which are evident in the last mile, i.e., one transfer and one stage less. Many public travellers prefer trips with fewer transfers and stages, namely, Choice A – walk and paratransit (first mile), then main stage (hitchhiking), and then, walking (last mile) – which results in 3 transfers and 4 stages. The other alternative combination, for relatively fewer travellers, is Choice B, which is characterised by 4 transfers and 5 stages, i.e., walking and MBT (first mile), and then, MBT and walking (last mile). Due to limited access or ownerships of private cars, all other mode combinations that comprise private cars are statistically insignificant. There is an array of factors (namely, demographic, socio-economic, activity, long-term decisions and trip) that influence these mode combinations. A few interesting factors, which are not statistically significant to influence the decisions for choosing modes, include gender, employment status, hitchhiking dissatisfaction, and personal/road safety of hitchhiking and paratransit. Remarkably, the long-term decisions associated with personal/road safety of hitchhiking are insignificant; instead, many are based on hearsay and/or rumours. Influencing factors suggest that future travel behaviours are likely to change, as human society is continuously transforming to seek better livelihoods, particularly urban-industrial and knowledge economies. Evidence lies in the uncommon usage of (existing) customised e-hailing services in rural South Africa. On the other hand, rural communities widely reveal high penetration of system technologies not limited to (public) transport services (for instance, shopping cards/vouchers). In addition, the lack of integrated public transport services and the violent competition therein, triggers the public travellers (for instances, hitchhikers) to constantly seek roadside hand signs to avoid disruptions by the MBT operators, and/or clearly and quickly request bypassing rides. There must be thorough additional socio-orientated studies to succinctly provide scenarios that will help maximise the societal benefits of mobility and/or expand the accessibility of (rural) South Africans to reach the desired socio-economic opportunities at efficient travel costs, where fair and professional multi-modal competition is promoted, service quality is encouraged, and the traveller-ride matching is seamless and efficient across trip transfers or stages. Reflection and recommendations: There are opportunities to explore and invest in transfer and intermodal transit systems in rural areas of lower-income countries. Although the travellers of all gender and age groups walk somewhere during their trips, the roads from their origins (i.e., the case study) are very poor, and cycling or driving during rainy days, for instance, will be almost impossible, as many become very muddy. Hitchhiking is by far the best alternative public transit service for long-distance trips in rural areas of lower-income countries, and it has developed characteristics of being completely irrational and dangerous, yet there is no statistical significance or empirical evidence to support this. In addition, there are different market niches for hitchhiking and paratransit. Nevertheless, the paratransit drivers/operators take measures that are arguably violent in the face of service competition. Essentially, competition needs to be broadly promoted instead of being seen as a (business) threat in the public transport industry. Furthermore, changing the status, i.e., from ‘free market' to formal, of the hitchhiking services could help mitigate some of the user challenges (e.g., disruption, lack of safety), and increase the government benefits (i.e., through road and/or parking pricing initiatives of the hitched rides) as well as the general economic growth of the area (i.e., attracting more operators and users, who might be avoiding the service, due to lack of specific policy coverage). Whilst on economic growth, seeking ways to formalise hitchhiking could help increase the labour force. Although traditional hitchhiking is still taking place, some of the public travellers have revolutionised hitchhiking, i.e., they are hitchhiking on social media. Unlike traditional hailing (i.e., on the roadside), social media thumbing is relatively safer, as the ride drivers are known. That is, tracking down such a driver is much easier and, if one attempts to do unsafe acts (as a driver), a profile of unsafe ride drivers (and other challenges) can be ‘dented' to alert future potential victims, e.g., through word-of-mouth, posts on the platforms, and some form of rating system. At present, the platform that entails the utilised datasets (i.e., Facebook), does not have the specific option to rate a profile/person, based on the service they provide, outside of what is called ‘marketplace' (i.e., interface specifically for advertising). Remarkably, this study is one of the guidelines for directing the development of ‘smart' (widely referring to sustainable) rural; the scholars state that the adoption and use of information technology has the potential to improve the economic welfare of rural areas. This approach is increasingly categorised as Mobility-as-a-Service (MaaS) or -Feature (MaaF). Precisely, for this case study, the rural root-causes of hitchhiking disruption need to be examined, include the perspective of MBT drivers/operators and the general rural communities (for instance, business, community organisations). In addition, the cost-benefit analyses will be necessary to guide the relevant and adequate adoption of public transport services that cater for the needs of diverse public travellers in the same areas. This process will further extend to other socio-economic dimensions (for example pathways, interchanges) that will reduce and mitigate social exclusions, spatial or territorial disparities and general hindrances to access socio-economic opportunities. In that light, the restructuring of policies, legislation, and strategies that governs mainstream institutions will be inevitable. Sadly, the institutions in lower-income countries are currently lacking the strategies to guide efficient and relevant tech-orientated transit services to all relevant key stakeholders. Notably, land use and transport play a crucial role in rural cohesion and revitalisation and, in part, the broad readdressing of gender disparity. rural cohesion and revitalisation comprise positive effects from improved socio-economic and land use or built environment, such as reduction in social exclusion and spatial/territorial disparities, increased access to social, economic, health and other livelihood opportunities. The net effect of female origin-destination patterns indicates migration from rural to urban areas, and rural-rural and urban-rural for their counterparts. Policy Implication: The best and urgently needed solution would be to develop and maintain investments ranging from as cheap as walking paths in these areas - which will be a lot more cost effective, unlike sourcing and maintenance of bicycles and making other forms of subsidies (e.g., feeder vehicles) – through to as expensive as the integration of Information and Communication Technology (like in Uber, Bolt – i.e., a single seamless digital platform (MaaS/MaaF). The above have direct impacts, at least, on the infrastructure development and transport planning and the relationships thereof, where polices are developed and implemented to inspire the development and maintenance of the stakeholder partnerships and entrepreneurial spirit in the whole public mobility supply chain, which also comprises inclusive strategies to attract the female population, who are currently in urban areas, to be more ‘attractive'. DA - 2026 DB - OpenUCT DP - University of Cape Town KW - Hitchhiking KW - Paratransit KW - e-Hailing KW - Mobility-as-a-Service/Mobility-as-a-Feature KW - Rural Mobility LK - https://open.uct.ac.za PB - University of Cape Town PY - 2026 T1 - Assessment of the demand for and influencing factors of hitchhiking, paratransit and e-hailing services in rural South Africa TI - Assessment of the demand for and influencing factors of hitchhiking, paratransit and e-hailing services in rural South Africa UR - http://hdl.handle.net/11427/43630 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/43630 | |
| dc.identifier.vancouvercitation | Serumula DM. Assessment of the demand for and influencing factors of hitchhiking, paratransit and e-hailing services in rural South Africa. []. University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Civil Engineering, 2026 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/43630 | en_ZA |
| dc.language.iso | en | |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Civil Engineering | |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject | Hitchhiking | |
| dc.subject | Paratransit | |
| dc.subject | e-Hailing | |
| dc.subject | Mobility-as-a-Service/Mobility-as-a-Feature | |
| dc.subject | Rural Mobility | |
| dc.title | Assessment of the demand for and influencing factors of hitchhiking, paratransit and e-hailing services in rural South Africa | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Doctoral | |
| dc.type.qualificationlevel | PhD |