Ride-hailing Companies Research Articles

Passenger engagement dynamics in ride-hailing services: A heterogeneous hidden Markov approach

Despite their current growth and future promise, ride-hailing companies struggle with brand loyalty. As a result, they spend heavily on various marketing tools, especially promotional offers, to encourage passenger engagement, which is often measured by how frequently passengers ride through their platforms. Although extensive research has investigated the passenger intention to continue using ride-hailing services, research that explicitly models the dynamics of passenger engagement is very scarce. In this research, we propose to capture passenger engagement dynamics in ride-hailing services and the factors contributing to them. We combine a heterogeneous hidden Markov model framework with Poisson regression models to probabilistically analyze the transition processes of passenger engagement. Specifically, we capture the influences of various promotional offers on the engagement transition probabilities. We conduct numerical experiments using real-world ride-hailing data. Results show that our model identifies inactive, occasional, and active engagement levels. Our coefficient estimates and sensitivity analysis show that giving moderately more promotional offers to inactively and occasionally engaged passengers would efficiently activate them. More importantly, we derive information about which promotional offers have more significant impacts on the passengers of different engagement levels.

Accept or reject a ride? This is the problem

PurposeOnline ride-hailing platforms match drivers with passengers by receiving ride requests from passengers and forwarding them to the nearest driver. In this context, the low acceptance rate of offers by drivers leads to friction in the process of driver and passenger matching. What policies by the platform may increase the acceptance rate and by how much? What factors influence drivers' decisions to accept or reject offers and how much? Are drivers more likely to turn down a ride offer because they know that by rejecting it, they can quickly receive another offer, or do they reject offers due to the availability of outside options? This paper aims to answer such questions using a novel dataset from Tapsi, a ride-hailing platform located in Iran.Design/methodology/approachThe authors specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that increase the acceptance rate. In this model, drivers compare the value of each ride offer with the value of outside options and the value of waiting for better offers before making a decision. The authors use the simulated method of moments (SMM) method to match the dynamic model with the data from Tapsi and estimate the model's parameters.FindingsThe authors find that the low driver acceptance rate is mainly due to the availability of a variety of outside options. Therefore, even hiding information from or imposing fines on drivers who reject ride offers cannot motivate drivers to accept more offers and does not affect drivers' welfare by a large amount. The results show that by hiding the information, the average acceptance rate increases by about 1.81 percentage point; while, it is 4.5 percentage points if there were no outside options. Moreover, results show that the imposition of a 10-min delay penalty increases acceptance rate by only 0.07 percentage points.Originality/valueTo answer the questions of the paper, the authors use a novel and new dataset from a ride-hailing company, Tapsi, located in a Middle East country, Iran and specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that could potentially increase the acceptance rate.

Factors Influence Passenger Experience on Ride-Hailing Transportation among Young Generation

Passenger experience is crucial to the success of the ride-hailing transportation industry, because passengers are more inclined to choose ride-hailing transportation companies or service providers that bring them a good experience. The purpose of this study is to examine the relationship between tangibility, reliability, responsiveness, assurance and passenger experience on ride-hailing transportation among students of Universiti Utara Malaysia (UUM). A total of 271 respondents participated in this study. The data were collected by online survey which was conducted through questionnaire and distributed using Google Form. This study analyzed by descriptive analysis, normality test, reliability analysis, Pearson correlation analysis, multiple regression analysis and hypothesis testing by using Statistical Package for Social Sciences (SPSS). The survey results show that there is relationship between tangibility, reliability, responsiveness, assurance with passenger experience. Ride-hailing companies or service providers need to improve their service quality to provide passengers with a good experience, in order to attract and retain passengers. In brief, the discussion and conclusion on the significance of this study were presented.

Locked in, logged out: pandemic and ride-hailing in South Africa and Kenya

AbstractThis article examines the impact of the pandemic on ride-hailing drivers and their mitigation strategies during lockdown in Africa. Ride-hailing has emerged as one of the latest paid-work opportunities for the continent's many unemployed. Yet, ride-hailing companies such as Uber and Bolt misclassify drivers to avoid regulation and responsibilities towards workers’ welfare. Drawing on 34 in-depth interviews with ride-hailing drivers, driver representatives and trade unions in South Africa and Kenya, this article makes two arguments. First, the gig economy in Africa provides work opportunities for the unemployed on the continent and simultaneously vitiates the working conditions through the commodification and informalisation of work. Second, the state-directed emergency measures act as a veneer to capital's efforts to commodify labour and the gig economy platforms have emerged as primary tools for it. Our account points to an urgent need for better regulatory systems to hold platform companies accountable and a collective bargaining mechanism in the gig economy.

An analysis of labor regulations for transportation network companies

There is a growing movement worldwide to regulate transportation network companies (TNCs) such as Uber and Lyft. This is driven by concerns over low driver wages. Two recent labor regulations that were passed in California are Assembly Bill 5 (AB5) and Proposition 22 (Prop 22). AB5 classifies drivers (and other gig-economy workers) as employees as opposed to contractors. The implication is that ride-hailing companies must pay drivers a minimum wage and associated benefits, and that the drivers work full-time. This negatively impacts the TNC business model in two ways: (a) wage effect: higher wages and benefits reduce profit margins significantly, and (b) flexibility effect: having a base of full time drivers reduces the ability of TNCs like Uber and Lyft to match driver supply to customer demand on a fine temporal scale. As an alternative, TNCs lobbied fiercely for Prop 22. This regulation offers drivers 120% of minimum wage, but only for driving hours spent servicing a trip (engaged time).This paper studies the impacts of these and other possible regulations using data from the city of San Francisco. We develop a market-equilibrium model that captures passenger demand and driver supply in response to TNC’s pricing decisions. We show that the flexibility effect under AB5 can be largely mitigated by offering staggered driver shifts. The wage effect is substantial and undermines the viability of the TNC business model. We find that the wage effect leads to a 18% decrease in TNC profit, whereas the flexibility effect results in a profit reduction ranging between 1%–10% depending on how shifts are designed. On the other hand, Prop 22 will preserve TNC profitability, but the guaranteed engaged time wage promised by Prop 22 is insufficient to materially increase driver wages. We show that this concern cannot be addressed by further increasing the engaged time wage, as it will increase passenger fares, increase driver cruising time, reduce efficiency, and significantly reduce TNC profit. We then suggest suitable modifications to each of the above regulations that can improve outcomes for drivers and passengers without jeopardizing the TNC business model.

Minimum Earnings Regulation and the Stability of Marketplaces

Problem definition: New York City and Seattle recently enacted minimum earnings regulations for ride-hailing providers that are based on their utilization rates. The regulations are intended to deliver minimum earnings while preserving the flexibility of the independent contractor model of work. Academic/practical relevance: This kind of regulation has the potential to impact marketplace stability, which we define as the ability of platforms to keep wages bounded while maintaining the current flexible (free-entry) work model. Methodology: We build a theoretical model to study the marketplace implications of this kind of regulation and identify precise conditions under which a utilization-based minimum earnings rule causes marketplace instability. We then calibrate our model using publicly available data, showing the extent to which the law can (or cannot) increase earnings while preserving both worker flexibility and marketplace stability. Results: For reasonable ranges of supply and demand elasticity, the law’s ability to increase earnings while maintaining the free-entry work model is quite limited, and even when earnings increases are achievable, they cause significant increases in driver idleness. Managerial implications: Given the law’s potential to cause instability, affected ride-hailing companies may need to respond to the law by reducing driver flexibility to limit supply. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1150 .

The cost of non-coordination in urban on-demand mobility

Over the last 10 years, ride-hailing companies (such as Uber and Grab) have proliferated in cities around the world. While generally beneficial from an economic viewpoint, having a plurality of operators that serve a given demand for point-to-point trips might induce traffic inefficiencies due to the lack of coordination between operators when serving trips. In fact, the efficiency of vehicle fleet management depends, among other things, density of the demand in the city, and in this sense having multiple operators in the market can be seen as a disadvantage. There is thus a tension between having a plurality of operators in the market, and the overall traffic efficiency. To this date, there is no systematic analysis of this trade-off, which is fundamental to design the best future urban mobility landscape. In this paper, we present the first systematic, data-driven characterization of the cost of non-coordination in urban on-demand mobility markets by proposing a simple, yet realistic, model. This model uses trip density and average traffic speed in a city as its input, and provides an accurate estimate of the additional number of vehicles that should circulate due to the lack of coordination between operators—the cost of non-coordination. We plot such cost across different cities—Singapore, New York (limited to the borough of Manhattan in this work), San Francisco, Vienna and Curitiba—and show that due to non-coordination, each additional operator in the market can increase the total number of circulating vehicles by up to 67%. Our findings could support city policy makers to make data supported decisions when regulating urban on-demand mobility markets in their cities. At the same time, our results outline the need of a more proactive government participation and the need for new, innovative solutions that would enable a better coordination of on-demand mobility operators.

The impact of ride-hail surge factors on taxi bookings

We study the role of ride-hailing surge factors on the allocative efficiency of taxis by combining a reduced-form estimation with structural analyses using machine-learning-based demand predictions. Where other research study the effect of entry on incumbent taxis, we use higher frequency granular data to study how location-time-specific surge factors affect taxi bookings to bound the effect of customer decisions while accounting for various confounding variables. We find that even in a unique market like Singapore, where incumbent taxi companies have app-based booking systems similar to those from ride-hailing companies like Uber, the estimated upper bound on the cross-platform substitution between ride-hailing services and taxi bookings is only 0.26. On the other hand, we show that incorporating surge price factor improves the precision of demand prediction by 12% to 15%. Our structural analyses based on a driver guidance system finds this improved accuracy in demand prediction reduces drivers’ vacant roaming times by 9.4% and increases the average number of trips per taxi by 2.6%, suggesting the price information is valuable across platforms, even if elasticities are low.

Regulating the algorithm: platform economy workers in China

Regulating the algorithm:platform economy workers in China Aidan Chau (bio) China makes an interesting case study for the global challenges of the platform economy. China's technology giants, such as Tencent and Alibaba, set the stage for high volumes of online commerce, and in this environment, other tech giants have thrived, notably food delivery companies Ele.me and Meituan, and ride hailing company Didi Chuxing. Platform workers face low wages, lack of labour protections, and working conditions dictated by algorithms. The political and social environment prevents independent organising and large-scale protests, exacerbated by the Covid-19 pandemic. China has one official trade union, the All-China Federation of Trade Unions (ACFTU) that is beholden to the Party and state, but recent reform efforts have led the ACFTU to try to focus on better representing platform workers. Further, the state has cracked down on technology companies and introduced regulations that attempt to strengthen workers' rights. China's platform economy China has the world's largest platform economy. A 'platform' is a tool that connects businesses, consumers, and others in a way not possible before the rise of the internet. Many platforms began as small websites or start-ups that expanded rapidly and resulted in a new flexible and casual employment model. In China, this began in 2008 with the spread of mobile technology. By 2014, mobile internet users surpassed those of computer users, meaning that many citizens accessed the internet for the first time on mobile phones and were ripe for app-based transactions. To avoid the effects of the global recession, China's economy became domestically re-orientated in 2008. Food delivery and ride hailing platforms experienced a rush of capital starting in 2013. The two ride-hailing companies Kuaidi and Didi received investments from Alibaba and Tencent, respectively. The food delivery platform Ele.me received a US$25 million capital investment from Sequoia Capital, and its rival Meituan got a US$300 million investment in 2014. The capital injection allowed these companies to set up their operations in all major cities in China within a year. Other economic conditions primed the platform economy to receive the necessary labour. The relative decline of China's manufacturing sector in the last five years has allowed an influx of workers. The new platform economy advertised good pay and flexible working conditions, which was attractive to those who formerly worked in factories. Statistics released by the companies reveal 15–30 percent of workers for these companies have come from manufacturing. Algorithms, labour and market share The rise of the platform economy in China has been rife with competition. Starting in 2014, the food delivery and ride hailing companies engaged in price wars by subsidising costs for their customers to expand their market shares. This competition resulted in Didi Chuxing buying Kuaidi and Uber China, and Ele.me purchased the third largest takeaway company, Baidu Waimai, maintaining a duopoly with Meituan ever since. Platform companies also compete with each other by worsening labour conditions for workers. Huge amounts of capital have been invested to develop algorithms that manage the allocation of labour, matching supply with demand and determining route planning. China has the world's largest platform economy. The new sector advertised good pay and flexible working conditions, which was attractive to those who formerly worked in factories In the food delivery industry, algorithms shorten the delivery time by fine-tuning the areas that workers are allocated to. In a pilot test conducted in 2019, the average distance travelled by a rider dropped by 5 percent. The algorithms also guide riders through the process of taking and dispatching orders, reducing the time workers spend waiting, planning and delivering the meals. For example, riders are directed by a heat map displaying the regional distribution of delivery orders. Workers follow the delivery route planned by the system, which is designed to find the fastest path for multiple deliveries. The combined effects of these algorithms have halved the delivery time of an average order from 1 hour in 2015 to 30 minutes in 20181. In the ride hailing industry, algorithms reduce the total time it takes for drivers to pick up passengers. Rather than...

Understanding the influence of user adaptation on the continuance intention towards ride-hailing services: the perspective of management support

This study investigates the influences of management support and behavioural adaptation on continuance intention to use ride-hailing services. Based on an extended expectation-confirmation model and by incorporating management support and user adaptation, this study proposes a research model and verifies the hypotheses using structural equation modelling with data gathered from face-to-face interviews of 472 user-drivers of dominant ride-hailing companies in Vietnam. The findings reveal that (1) perceived usefulness, satisfaction and behavioural adaptation have a determinant impact on continuance intention, (2) while management support has no significant direct association with continuance intention, the link is fully mediated by behavioural adaptation, and (3) user adaptation is also verified to be a partial mediator of the relationship of perceived usefulness and continuance intention. Both theoretical and managerial implications are provided to broaden the current understanding of the adaptation literature, suggesting that firms build stronger relationships with their user-drivers, thus sustaining firm success.

Analyzing travelers' attitude towards ride-hailing services in developing countries: Case of Lahore, Pakistan

Ride-hailing services are gaining momentum to meet the urban travel demand in the absence of proper public transport in developing countries. To the best of our knowledge, research on passenger's behavior and attitude towards the service quality of ride-hailing services and certainly studies focusing on developing countries are quite rare. Ride-hailing services were launched to meet the travel demand of residents throughout the city of Lahore, Pakistan in 2015. A revealed preferences study of 865 respondents consisting their demographic, travel information and perceptions related to selected attributes of service quality was carried out to examine their attitude and behavior towards the use of ride-hailing services in Lahore, Pakistan.Most of the users of these services are young, well-educated and have relatively high income. The travelers who were using Rickshaw services before are found to be the frequent users of these services and ride-hailing services have started replacing respondents' previous modes of travel. Exploratory factor analysis were employed on collected data to identify the factors effecting the traveler's perceptions regarding the use of ride-hailing services and structural model of users' satisfaction were constructed based on these factors later.The results exploratory factor analysis (EFA) and structural equation modeling (SEM) revealed that significant determinants of passenger's satisfaction with ride-hailing services are coverage and accessibility attributes (CAA), instrumental attributes (IA), service attraction attributes (SAA) and safety attributes (SA). The number of trips, trip purpose, income, education, time since the use of these services, profession and household size are also significant parameters in the determination of traveler's behavior and attitude towards the use of ride-hailing services in Lahore city. The commuter's overall satisfaction with service quality of app-based services have positive association with travelers intentions to continue use of app-based services and their believe that these services have filled the gap of public transport in the city and replacing previous modes of travel. The further improvements in attributes will enhance the traveler's satisfaction with these services. Insight into the attitude and perceptions of travelers would be useful for the transport planners, ride-hailing companies and policy makers for making appropriate improvements in the services and giving them space in existing transport system.

What makes passengers continue using and talking positively about ride-hailing services? The role of the booking app and post-booking service quality

Ride-hailing services are increasingly consolidating their role in the transport sector in low- and middle-income countries where there is limited investment in public transport. However, much is unknown about the determinants of ride-hailing service use and quality of the service. The present study investigates the direct and indirect influences of perceived quality of ride-hailing service including perceived booking app and perceived post-booking service quality on continuous usage intention and word-of-mouth (WOM) of ride-hailing passengers. Emerging research has aimed to understand the hierarchical structure of ride-hailing service quality (including booking app and post-booking service). Therefore, this study proposes a formative hierarchical component model of perceived booking app quality consisting of seven dimensions (i.e., privacy and security, ease of use, functionality, design, information accuracy, route detection, and service). Likewise, the perceived post-booking service quality is comprised of four dimensions (i.e., reliability, personal, convenience, and tangibility). Data used for testing the model was collected from 536 ride-hailing service users in Ho Chi Minh city, Vietnam. The results provide insights into attributes forming perceived quality of ride-hailing booking apps and perceived post-booking service quality and how these constructs affect passenger loyalty. The results are also useful for ride-hailing companies in their efforts to prioritise critical service attributes and ensure their service quality meets or exceeds passengers’ expectations.

Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand

With the rapid growth of the mobility-on-demand (MoD) market in recent years, ride-hailing companies have become an important element of the urban mobility system. There are two critical components in the operations of ride-hailing companies: driver–customer matching and vehicle rebalancing. In most previous literature, each component is considered separately, and performances of vehicle rebalancing models rely on the accuracy of future demand predictions. To better immunize rebalancing decisions against demand uncertainty, a novel approach, the matching-integrated vehicle rebalancing (MIVR) model, is proposed in this paper to incorporate driver–customer matching into vehicle rebalancing problems to produce better rebalancing strategies. The MIVR model treats the driver–customer matching component at an aggregate level and minimizes a generalized cost including the total vehicle miles traveled (VMT) and the number of unsatisfied requests. For further protection against uncertainty, robust optimization (RO) techniques are introduced to construct a robust version of the MIVR model. Problem-specific uncertainty sets are designed for the robust MIVR model. The proposed MIVR model is tested against two benchmark vehicle rebalancing models using real ride-hailing demand and travel time data from New York City (NYC). The MIVR model is shown to have better performances by reducing customer wait times compared to benchmark models under most scenarios. In addition, the robust MIVR model produces better solutions by planning for demand uncertainty compared to the non-robust (nominal) MIVR model.

A Study of Spatiotemporal Distribution of Mobility-On-Demand in Generating Pick-Up/Drop-Offs Location Placement

The location placement of pick-up/drop-offs of ride hailing usually only considers spatial distribution within a certain area. Previous studies often mapped out the potential hotspots for pick-up/drop-offs, benefitting the ride-hailing company and not considering the passengers. Therefore, in this study, we incorporated the spatiotemporal distribution of mobility-on-demand on generating pick-up/drop-off location placement using a genetic algorithm considering the walking distance and minimum demand data served within the radius. The data collected are analyzed through the clustering method, and the resulting cluster centers are fed into the location optimization algorithm. The genetic algorithm is used to optimize the location placement with the consideration of the user’s walking distance and demand. The algorithm is able to find an appropriate placement and determine reliable pick-up/drop-off stations within the study area based on observed spatiotemporal demand despite the difference in demand distribution through different time periods.

A modified artificial bee colony algorithm for the dynamic ride-hailing sharing problem

Ride-hailing sharing involves grouping ride-hailing customers with similar trips and time schedules to share the same ride-hailing vehicle to reduce their total travel cost. With the current information and communication technology, ride-hailing customers and drivers can be matched in real-time via a ride-hailing platform. This paper formulates a dynamic ride-hailing sharing problem that simultaneously maximizes the number of served customers, minimizes the travel cost and travel time ratios, and considers the capacity, time window, and travel cost constraints. The travel cost ratio is the ratio of actual passengers’ fare to the passengers’ fare without ride-hailing sharing, whereas the travel time ratio is defined as the actual travel time (including waiting time) over the maximum allowable travel time. To solve the dynamic problem, it is divided into many small and continuous static subproblems with an equal time interval. Each subproblem is solved by a modified artificial bee colony (MABC) algorithm with path relinking, while the contraction hierarchies and vantage point tree are used to determine the shortest path and accelerate the algorithm, respectively. Problem properties and the performance of the proposed solution method are demonstrated using large-scale real-time data from Didi that is the largest ride-hailing company in China. The proposed method is shown to outperform the benchmark, i.e., greedy randomized adaptive search procedure (GRASP) with path relinking. The proposed method also performs better when the length of each time interval is longer, and the tolerance for the incremental travel time caused by detours is higher. We also demonstrate that (a) considering both travel cost and travel time ratios in the objective can achieve a better sharing percentage, and balance the increase in the travel time ratio and the decrease in the travel cost ratio compared with the objective that misses either travel time or the travel cost ratio; and (b) the passengers can gain a large out-of-pocket cost saving in the case of ride-hailing sharing while enduring a relatively small increase in travel time compared with the case without ride-hailing sharing.

Using spatio‐temporal deep learning for forecasting demand and supply‐demand gap in ride‐hailing system with anonymised spatial adjacency information

To reduce passenger waiting time and driver search friction, ride-hailing companies need to accurately forecast spatio-temporal demand and supply-demand gap. However, due to spatio-temporal dependencies pertaining to demand and supply-demand gap in a ride-hailing system, making accurate forecasts for both demand and supply-demand gap is a difficult task. Furthermore, due to confidentiality and privacy issues, ride-hailing data are sometimes released to the researchers by removing spatial adjacency information of the zones, which hinders the detection of spatio-temporal dependencies. To that end, a novel spatio-temporal deep learning architecture is proposed in this paper for forecasting demand and supply-demand gap in a ride-hailing system with anonymized spatial adjacency information, which integrates feature importance layer with a spatio-temporal deep learning architecture containing one-dimensional convolutional neural network (CNN) and zone-distributed independently recurrent neural network (IndRNN). The developed architecture is tested with real-world datasets of Didi Chuxing, which shows that our models based on the proposed architecture can outperform conventional time-series models (e.g., ARIMA) and machine learning models (e.g., gradient boosting machine, distributed random forest, generalized linear model, artificial neural network). Additionally, the feature importance layer provides an interpretation of the model by revealing the contribution of the input features utilized in prediction.

Jeddah City Ride-hailing Company Bus Routes Planning by Using Traffic Neural Network Design

This study aims to discover the relationship between neural systems and their use and nature in optimizing public transportation in real life. As neural networking system in the human body represents a smooth delivery and reception of data between the neural joints, in similarity to public transportation delivering public to multiple points/zones. Today's simulation systems depend on simulating real life to help us find an optimized solution but never the ultimate optimization. Applying an intelligent design like the human neural system into real life will deliver an ultimate solution to such a problem. Applying a neural networking system to Jeddah's traffic platform allowed us to reach an optimized map/network for the public transportation services with clear stations/stops numbers and real-life path/lane to be followed.

Parameter identification for the decision model of uncertainty price competition in food delivery services

The sharing economy innovation has created a new service industry sector, the ride-hailing industry. Government regulations regarding minimum tariffs change the competition map and uncertainty in that industry. This has led to predatory pricing, switching costs and pre-emptive duopoly. This study aims to determine the parameters that can affect the decision model in the uncertainty of price competition through factorial experiments. This study was conducted on two ride-hailing companies, which currently lead market share, thus establishing a duopoly market. There are many services offered by these companies. This study focuses on investigating food delivery services, which are one of the stable services during the Covid-19 pandemic. The approach that will be used in this research is a factorial experiment to determine what parameters can affect price determination with several uncertainty cases. The result is the identification of the optimal decision model, both parameters and levels in pricing. The implication of this study is fair competition for the welfare of society and the country.

What does ride-hailing mean for parking? Associations between on-street parking occupancy and ride-hail trips in Seattle

Ride-hailing companies, including Uber and Lyft, upset the traditional nexus between driving and parking. As cities consider parking policy reforms amidst a wave of app-based transportation systems, including ride-hailing, the associations between parking occupancy and ride-hailing remain unclear. Examining this association is critical as it may help understand the connections between ride-hailing and the built environment and help cities plan for a future of new transportation technologies that may alter the role of or need for on-street parking. This article examines associations between ride-hail trips and on-street parking occupancy in Seattle, Washington. We use Uber and Lyft trip data combined with parking and built environment data to model projected parking occupancy in Seattle at both current and increased levels of ride-hailing. Results demonstrate that current rates of ride-hailing are associated with higher on-street parking occupancy; in other words, areas with high parking occupancy also have large numbers of ride-hail trips. The positive association may be due to several factors such as travelers using ride-hailing to avoid hunting for scarce on-street parking. While some travelers may substitute personal driving (and parking) for ride-hailing, results suggest that freed up parking is utilized by other travelers who switch from other times, modes, or make trips that they otherwise would not have. The association between on-street parking occupancy and ride-hail trips is, however, not linear. We predict that on-street parking occupancy will start to decline if or when ride-hail trips are about 2.24 times greater than the average number of trips taken in 2016. Flexible and adaptable parking policies that can more quickly respond (or better yet, be proactive) to changing ride-hail demand will better manage the changes that may affect on-street parking.

The Employment Status of Nigerian Workers in the Gig Economy: Using Uber as a Case Study

Owing to strides in technology like mobile applications and online platforms, thousands of Nigerian workers now earn a decent income through 'gig" work, which affords them the flexibility to set their own hours and choose which jobs to take or not to take. This growth, which has become dominant in the last five to seven years, has however sparked concerned over the lack of rights and status given to these workers in such industries. In May 2017, Uber cut fares by 40%, thereby triggering drivers strike in Lagos Nigeria commercial city. In Abuja, Nigeria capital city, low fares in addition to the high commission rates have been an ongoing tussle between Uber and their drivers. [1] According to one aggrieved driver, ‘They don’t even consult with us. What is the essence of a partnership if decisions are one way? [2] More recently the Lagos state Government had a round table discussion with these tech companies and thereafter introduced stringent regulatory measures for ride hailing companies such as Uber, Taxify (now Bolt) to mention a few. However, this did not sit well with the drivers of these platforms who have been absent from the negotiations. They expressed dissatisfaction at not being invited to the round table discussion, citing the fact that these ride-hailing companies never saw them as employees but merely as contractors. [3] This article seeks to examine the employment status of these gig workers in Nigeria. It suggests that introducing a third force, i.e a new category of workers called “the independent employee” that would fit into the modern day economy may provide the much-needed solution to this lacuna. Keywords: Employment, Gig workers, Nigeria, Independent Employee DOI: 10.7176/JLPG/107-08 Publication date: March 31 st 2021 [1] Yomi Kazeem, ‘Uber has a new problem in Nigeria – driver stikes in Abuja.’ Quartz Africa. 2016 > https://qz.com/africa/823361/uber-drivers-in-abuja-are-going-on-strike-to-protest-low-fares/ > accessed 1 st June 2020 [2] National Union of Professional E-Hailing Driver Partners (NUPEDP). Facebook page 2018 > https://www.facebook.com/nupedp.nupedp.1 > accessed 6th June 2020 [3] Emmanuel Paul, ‘What you need to know as Lagos plans to enforce regulation on uber, Bolt(Taxify) others’ https://techpoint.africa/2020/03/03/lagos-regulation-ride-hailing/ > accessed 2 April 2020