On-demand Mobility Services Research Articles

Alternative futures for shared mobility systems: impacts on transport planning tools and techniques

On-demand mobility services are changing the way we move in cities, fostered by digitalisation, vehicle automation and Mobility-as-a-Service (MaaS) platforms. Transport planning tools and techniques are expected to keep up to date to support decision-makers on the successful integration of these new mobility options in the urban transport mix. However, the evolution of the emerging mobility solutions is highly uncertain, as it is a function of complex factors that go beyond the influence of transport stakeholders. This poses a challenge for the definition of the requirements that data analysis techniques and modelling tools will face. This paper presents a series of explorative scenarios that provide different contexts for the evolution of urban mobility in Europe, in order to grasp the plausible pathways that shared mobility services will follow in the upcoming decades. The scenarios are adapted from those developed by the climate change research community and are used in a Delphi poll to gather expert visions on how shared mobility services will evolve. The availability of a range of possible futures for shared mobility facilitates the identification of the capabilities that transport decision-makers will demand from data analysis techniques and modelling tools.

Mobile application for bus operations controlled by passengers: A user experience design project (UX)

The bus system accounts for a significant part of trips in the city of Sao Paulo. However, in recent years the percentage of users has been falling concerning the growth modes such as individual transport and the new modes of transport on-demand based on applications. The loss of attractiveness is associated with users' negative perceptions of the quality of the service provided. One of the complaints is the recurring occurrences of buses that do not halt at the stop, even when the user signals his intention to embark or disembark. One way to address this problem would be to use technology like on-demand mobility services based on mobile applications. Although the idea itself seems to be simple, there is no report to date about a fixed route bus service that has implemented a bus hailing application that allows the user the possibility of requesting a bus to halt at a given stop. This paper proposes a new operation model of fixed-route buses that ensures the user requests fulfillment. The proposed model consists of functionalities that make the bus user visible to the operation, creating a more reliable connection between drivers and users. We evaluated the specified functionalities by a group of typical bus users in Sao Paulo, using a User Experience (UX) method. The evaluation clearly showed users' needs and desires regarding the features they would appreciate in the application. We collected clear evidence of the usefulness or not of the improvements suggested by users. Based on the study, we concluded that users agreed with the need to have greater control over their bus trips, in particular, to ensure that their intentions are adequately served and have a confirmation of the recognition of their requests. The study may become an interim solution that may be applied to existing bus services by taking them closer to on-demand services and MaaS (Mobility as a Service).

An optimization-based planning tool for on-demand mobility service operations

Regions worldwide are adopting and exploring low-speed automated electric shuttle (AES) service as an on-demand shared mobility service in dense geofenced urban areas. Building on this concept, the National Renewable Energy Laboratory (NREL) recently developed the Automated Mobility District (AMD) toolkit. The AMD toolkit—comprising of a travel micro-simulation model and an energy estimation model—estimates the mobility and energy impacts of a given shuttle configuration within an AMD. Early-stage AMD deployments need to find optimal operational configurations that include: (a) passenger capacity of an AES, (b) time-dependent routes, and (c) fleet size (AES units) to satisfy the demand for the region. This research extends the AMD toolkit functionality by developing an optimization-based planning module that will assist in the operations of AES units. We developed a constrained mixed-integer program accounting for passenger waiting time, battery range, and passenger capacity of AES units. For scalability, we demonstrated the Tabu search-based solution technique for a real-world network—a proposed AMD deployment in Greenville, South Carolina, USA. Compared to rule-based operations, our developed solution yields higher travel time and energy savings for the network at different demand levels. The sensitivity analyses for waiting time thresholds indicate nonlinearity in the system performance, underscoring the need to meet shared-use mobility user-level expectations. The developed optimization framework can be adapted and extended to accommodate different categories of shared-use on-demand mobility services.

A generalized diffusion model for preference and response time: Application to ordering mobility-on-demand services

The goal of this research study is to model user preferences and response times (RTs) jointly in the context of Mobility-on-Demand (MOD) services under different MOD operator pricing schemes, information frames, and pressure levels. MOD operators’ information provision, delay, and vehicle allocation strategies influence users’ preferences and RTs, which, in turn, affect which operational and information provision strategies are optimal for MOD operators. Evidence shows that preferences and RTs are sensitive to precedent decisions, information frames, risk, and time pressure. These dynamic interplaying factors are challenging to capture using a traditional discrete choice modeling framework. Hence, this study proposes a generalized diffusion model based on Decision Field Theory (DFT), multi-attribute Prospect Theory (PT), and Random Utility Theory to model these various interplaying factors. This study applies the proposed modeling approach in the context of ordering Shared-use Automated Vehicle Mobility Services (SAMS). Sensitivity analyses explore the impacts of various inputs and model parameters such as initial waiting time estimate, updated waiting time estimate, time pressure, loss aversion, and value-of-time on preferences and RTs. The proposed model can provide value to MOD operators in terms of information provision and pricing strategies. Moreover, the proposed model can assist policymakers and planners interested in the system impacts of MOD services and regulating MOD information provision and pricing strategies. The modeling framework can extend to other applications where multiple sub-decisions are necessary to make a single (travel) choice under information update, framing, risk, and time pressure.

Using data and technology to integrate mobility modes in low-income cities

ABSTRACT The utilisation of data and technology in transportation has given rise to new forms of flexible, on-demand mobility and urban freight services. Such advances have also enabled the integration of multiple transportation modes and facilitated more predictable, environmentally-friendly, high-volume trips. While many of these technologies are now commonplace in higher income cities, they are less prevalent in lower income cities. This review identifies a) recent advances in technology and data that can facilitate multimodal urban travel, and b) the existing and potential application of these advances in low- and lower middle-income (LIC and LMIC) cities, particularly in Sub-Saharan Africa and Asia. This review examines peer-reviewed and grey literature and uses a multi-method approach with expert interviews. The review identifies open transportation data and data standards, global positioning systems, cashless fare payment, and blockchain as having high potential to facilitate integrated mobility in lower income contexts, and considers potential barriers to adopting and risks of utilising these technologies in LICs and LMICs. The review finds that, enabled by technology and data advances, LICs and LMICs could leapfrog the high car use and inefficient urban freight systems typical of higher income cities to more integrated, efficient, multimodal transport systems.

Strategic Planning for Integrated Mobility-on-Demand and Urban Public Bus Networks

App-based services and ridesharing in the field of mobility-on-demand (MoD) create a new mode of transport between motorized individual transport and public transportation whose long-term role in the urban mobility landscape and within public transport systems is not fully understood as of today. For the public transport industry, these new services offer new chances but also risks, making planning models and tools for integrated intermodal network planning indispensable. We develop a strategic network planning optimization model for bus lines that allows for intermodal trips with MoD as a first or last leg. Starting from an existing public transport network, we decide simultaneously on the use of existing line segments in the future fixed-route network, on areas of the city where an integrated MoD service should be offered, on how MoD interacts with the fixed-route network via transfer points, and on passenger routes fulfilling given service-level requirements. The main challenges from a modeling point of view are to capture the interplay between MoD services and the fixed public network, as well as the approximation of MoD costs taking into account that vehicle utilization is a key factor influencing these costs. We develop a path-based formulation and a branch-and-price algorithm, as well as an enhanced enumeration-based approach, to solve real-world instances to proven optimality. The solution methods are tested on instances generated with the help of real-world data from a medium-sized German city, Göttingen, that currently operates around 20 bus lines.

Short-Term Demand Forecasting for on-Demand Mobility Service

To improve the mymargin efficiency of urban on-demand mobility services (OMS) (e.g., taxi and for-hire vehicles such as Uber, Lyft, and Didi), it is important to frame proactive operation strategies before the actual demand is revealed. The task is challenging since the effectiveness depends on the knowledge of passenger demand distribution in immediate future and is prone to prediction errors. In this study, we develop the boosting Gaussian conditional random field (boosting-GCRF) model to accurately forecast the distribution of short-term future OMS demand using historical OMS demand data. Comprehensive numerical experiments are conducted to evaluate the performance of boosting-GCRF as compared to four other benchmark algorithms. The results suggest that the boosting-GCRF is superior with the best mean absolute percentage error being 14%. In addition, the model is found to be robust under demand anomalies, and the density functions generated by the boosting-GCRF model are found to well capture the actual distribution of the short-term taxi demand.

Adoption of Exclusive and Pooled TNC Services in Singapore and the US

On-demand mobility services provided by transport network companies (TNCs) have experienced significant growth in their adoption and diversification of services in major metropolitan cities around the world. This study analyzed primary data from Singapore to explore the sociodemographics of TNC users and determine who among TNC users is more likely to pool their trips and what modes these services are replacing. We compared these results with a comprehensive literature review of similar studies of TNC users in the metropolitan US. We found that the sociodemographics of TNC users in general are similar in Singapore and the US: younger, highly educated, and higher income individuals are more likely to have used TNC services. On the other hand, when differentiating by type of TNC service, we found that younger individuals from households that do not own a car are more likely to have pooled in Singapore, whereas employment is an important predictor in the US. We also found differences in mode substitution; whereas TNC trips in the US primarily induce additional trips or replace trips by public and non-motorized transport, in Singapore they primarily replace personal/private vehicle trips. In Singapore, we explored mode substitution by exclusive and pooled TNC services separately, and found that pooled trips draw more from public and nonmotorized transport, whereas exclusive trips replace more personal/private vehicle trips. These results suggest that people in Singapore view exclusive and pooled TNC services as distinct travel options that may be more closely related to other private or public transport, respectively. Differences between Singapore and the US highlight the importance of accounting for local context and suggest that the quality of all travel alternatives in the urban area will affect the mode substitution of TNC trips.

Efficient proactive vehicle relocation for on-demand mobility service with recurrent neural networks

One major challenge for on-demand mobility service (OMS) providers is to seamlessly match empty vehicles with trip requests so that the total vacant mileage is minimized. In this study, we develop an innovative data-driven approach for devising efficient vehicle relocation policy for OMS that (1) proactively relocates vehicles before the demand is observed and (2) reduces the inequality among drivers’ income so that the proactive relocation policy is fair and is likely to be followed by drivers. Our approach represents the fusion of optimization and machine learning methods, which comprises three steps: First, we formulate the optimal proactive relocation as an optimal/stable matching problems and solve for global optimal solutions based on historical data. Second, the optimal solutions are then grouped and fed to train the deep learning models which consist of fully connected layers and long short-term memory networks. Low rank approximation is introduced to reduce the model complexity and improve the training performances. Finally, we use the trained model to predict the relocation policy which can be implemented in real time. We conduct comprehensive numerical experiments and sensitivity analyses to demonstrate the performances of the proposed method using New York City taxi data. The results suggest that our method will reduce empty mileage per trip by 54–70% under the optimal matching strategy, and a 25–32% reduction can also be achieved by following the stable matching strategy. We also validate that the predicted relocation policies are robust in the presence of uncertain passenger demand level and passenger trip-requesting behavior.

The sharing economy and the job market: the case of ride-hailing drivers in Chile

Ride-hailing (ridesourcing) companies such as Uber, Lyft, and Didi Chuxing have been a disruptive force in the urban mobility landscape around the world during the past decade. In this paper, we analyse the working conditions, earnings, and job satisfaction of ride-hailing drivers. We begin by discussing the regulatory, labour, financial, and urban mobility effects of ride-hailing companies. Then, we present the results of a self-administered survey to ride-hailing drivers in Chile, which is complemented with the use of online tools for the estimation of driving earnings. Our findings show that the flexibility to choose work times is the most appreciated attribute of this job, even though most drivers follow a somewhat fixed routine each week. By contrast, the level of transparency with which ride-hailing apps determine driver pay is the attribute with the lowest satisfaction score. A large number of respondents drive for long daily and weekly periods, which is a health and safety hazard. Current drivers are not concerned about the future deployment of driverless vehicles for on-demand mobility services. Ordered probit models for job satisfaction show that ride-hailing was better evaluated by drivers who use it as a complement to another part-time job, by those who earn more money per week, and by those who have not experienced undesirable situations while working, such as harassment or traffic crashes.

A Predictive Fleet Management Strategy for On-Demand Mobility Services: A Case Study in Munich

The global market for MoD services is in a state of rapid and challenging transformation, with new market entrants in Europe, such as Uber, MOIA, and CleverShuttle, competing with traditional taxi providers. Rapid developments in available algorithms, data sources, and real-time information systems offer new possibilities of maximizing the efficiency of MoD services. In particular, the use of demand predictions is expected to contribute to a reduction in operational costs and an increase in overall service quality. This paper examines the potential of predictive fleet management strategies applied to a large-scale real-world taxi dataset for the city of Munich. A combination of state-of-the art dispatching algorithms and a predictive RHC optimization for idle vehicle rebalancing was developed to determine the scale by which a fleet size can be reduced without affecting service quality. A simulation study was conducted over a one-week period in Munich, which showed that predictive fleet strategies clearly outperform the present strategy in terms of both service quality and costs. Furthermore, the results showed that current taxi fleets could be reduced to 70% of their original size without any decrease in performance. In addition, the results indicated that the reduced fleet size of the predictive strategy was still 20% larger compared to the theoretical optimum resulting from a bipartite matching approach.

Dynamics of travelers’ modality style in the presence of mobility-on-demand services

Modality style –defined as a set of frequent travel modes characterizing the travelers’ habits, routines, and predispositions– is a key player in forming dynamics of travelers’ mode choice behavior. This study aims to uncover the dynamics of modal preferences while the Mobility-on-Demand (MoD) services operate in the market. Using the 2017 National Household Travel Survey data, a Multiple Discrete Continuous Extreme Value model is developed to analyze the dynamics of individuals’ modality style. This model enables us to take into consideration marginal rates of substitutions between different transportation modes. Variables of interest in this analysis include the frequency of use of mobility-on-demand (MoD) services as well as the frequency of walking, biking, transit, and auto trips over the course of a month. The results of this study offer city planners and policymakers an opportunity to better understand the factors underlying modality styles, and which priorities to focus on when designing a sustainable development plan for resident-centric Smart Cities. According to the results, age, work status, education, auto availability, and the built environments are among the significant contributors to the modality styles. The results also indicate that the extent of the substitution relationship between transit and MoD services is highly context dependent.

Mobility-on-demand: An empirical study of internet-based ride-hailing adoption factors, travel characteristics and mode substitution effects

Ride-hailing services are shaping travel behaviours and emergent urban mobility patterns. From their initial diffusion centres in North America and Europe, these on-demand mobility services are increasingly becoming available in developing countries. Yet, empirical research from these contexts on the impact of ride-hailing services is lacking. To address this gap, this paper examines the factors driving the adoption of ride-hailing and the associated travel characteristics and mode substitution effects in Ghana, Sub-Saharan Africa. Using data from a large sample survey (N = 1188) of commuters in a multi-variable structural equation model, the paper shows that socio-demographic factors, perceived benefits and ease of use of ride-hailing, perceived safety risks and car-dependent lifestyles influence adoption and use of ride-hailing services. Similar to other contexts, individuals’ reference ride-hailing trips were mainly for ‘special occasion’ purposes (51%), but work and school journeys were also high (41%). Shorter travel times (≤30 min) and single passenger journeys within inner-suburban and outer-suburban localities typify ride-hailing trips. This contrasts with other contexts where ride-hailing is used frequently by urban dwellers and less so by those in the suburbs. Ride-hailing use replaced conventional taxis (51%), public transport (36%), private car (10%) and walking (1%), suggesting mode substitution effects for individuals’ reference trips. Further exploration of a full day’s travel mode choices also revealed that individuals use other available modes of transport in addition to ride-hailing services. However, multi-modal integration is weak, suggesting that ride-hailing tends to be used alone for full door-to-door journeys, instead of complementing other existing modes in serving first/last mile access for example. The implications of the findings for sustainable mobility are discussed.

Optimizing dynamic switching between fixed and flexible transit services with an idle-vehicle relocation strategy and reductions in emissions

Abstract A novel approach to dynamic switching service design based on a new queuing approximation formulation is introduced to systematically control conventional buses and enable provision of flexible on-demand mobility services. This new approach guides smart vehicles in a service area that needs last mile transit services via either traditional buses, which provide fixed-route services, or flexible-route on-demand mobility services. We also include dynamic pre-positioning of idle vehicles in anticipation of new customer arrivals, and relocation of vehicles to rebalance the use of vehicles in the system, which can have a sizable effect on energy and environmental conservation. Using a New York City transit dataset, the proposed strategy for non-myopic switching between flexible-route and fixed-route service and re-positioning of idle vehicles improves social welfare by up to 32%, while the impact of the proposed strategy on vehicle miles traveled is shown to be as high as 53% over that of the current transit service.

The Value of Connectivity in the Automotive Sector – a First Look

This study looks at the actual value that connectivity in vehicles can bring to society and the different sectors affected, Connectivity is one of the four critical megatrends facing the automotive industry. These trends are autonomous driving, shared mobility and electrification, yet connectivity is both separate and integrated to these other megatrends. The importance of connectivity is therefore fundamental to the future evolution of the industry. The vehicle is emerging as the next digital platform which may be analogous to the evolution of the smartphone and the opportunities that connectivity provides has incentivized automakers to increasing install embedded connectivity to capture the value from this new ecosystem. Such vehicles are often termed connected vehicles. One estimate from McKinsey Advance Industries (2016) predicts that on-demand mobility and data-driven services from such vehicles could account for approximately $2 trillion (or approximately 30%) of the automotive revenue pool by 2030, with data connectivity services accounting for between $450-$750 billion per year. This growth is built upon advanced mobile telecommunication standards, in particular, cellular standards, which provide the enabling infrastructure for new connectivity-based products and services to emerge in the automotive/mobility sector. Below are highlighted results from the study: • Connectivity is a megatrend that is transforming the automotive industry towards a new mobility sector. In the near future, most new vehicles will be increasingly equipped with embedded connectivity capabilities due to safety regulations, development of V2X functionality, and the potential service revenue. • The growing value of connectivity in the automotive sector is predicated on the development of high performance, open telecommunication standards, such as advanced cellular technology (e.g. 4/5G). • The vehicle is becoming the next big digital platform, generating competition between the existing mobile/smartphone ecosystem and the emerging vehicle ecosystems for control of the value of automotive/mobility services. • Connected vehicle applications are still small but are growing. Current estimates of the revenue from the vehicle ecosystem to automakers were calculated at $670 (US) and $593 (WW) per connected vehicle, based on a subset of existing applications in 2018. • The total revenue from connectivity-enabled products and services in the automotive sector was calculated to grow from $223 billion to $483 billion from 2018-2023 for a subset of existing revenue pools, with forecasts predicting as much as $2 trillion by 2030. • Market revenues do not provide the whole picture, especially when multi-sided business models are deployed. Therefore, the total economic value, including consumer surplus and relevant externalities, is important to bear in mind when determining the value of connectivity in the automotive sector or regulation of the sector.

The Autonomous Mobility Innovation Lifecycle

The driverless future promised by next-generation mobility will be made possible by two new technologies: on-demand mobility services, such as ride-hailing and last-mile delivery, and autonomous vehicles (AVs), which can be used for consumer transportation and logistics. An innovation and its adoption advance along a fourphase lifecycle, and we can evaluate the performance of these two driverless mobility technologies using distinct progressions. This article presents an analysis of the AV innovation lifecycle and introduces four dimensions by which to measure its performance over time. Finally, it reveals five requirements that must be addressed before AV use can be scaled for consumer transportation and logistics.

A Dynamic Routing Framework for Shared Mobility Services

Travel time in urban centers is a significant contributor to the quality of living of its citizens. Mobility on Demand (MoD) services such as Uber and Lyft have revolutionized the transportation infrastructure, enabling new solutions for passengers. Shared MoD services have shown that a continuum of solutions can be provided between the traditional private transport for an individual and the public mass transit-based transport, by making use of the underlying cyber-physical substrate that provides advanced, distributed, and networked computational and communicational support. In this article, we propose a novel shared mobility service using a dynamic framework. This framework generates a dynamic route for multi-passenger transport, optimized to reduce time costs for both the shuttle and the passengers and is designed using a new concept of a space window. This concept introduces a degree of freedom that helps reduce the cost of the system involved in designing the optimal route. A specific algorithm based on the Alternating Minimization approach is proposed. Its analytical properties are characterized. Detailed computational experiments are carried out to demonstrate the advantages of the proposed approach and are shown to result in an order of magnitude improvement in the computational efficiency with minimal optimality gap when compared to a standard Mixed Integer Quadratically Constrained Programming-based algorithm.

“Three Ps in a MOD:” Role for mobility on demand (MOD) public-private partnerships in public transit provision

The growing number of public transportation agencies partnering with Mobility on Demand (MOD) or Mobility as a Service (MaaS) companies raises the question of what role MOD companies can, should, and currently play in the provision of public transport. In this article, we develop a typology reflecting 62 MOD public-private partnerships (MOD PPPs) in the United States and present lessons learned. We conducted 34 interviews with representatives from four MOD companies and 27 public agencies. The interviews spanned October 2017 to April 2018. The resulting MOD PPP typology consists of four service models: 1) First-Mile/Last-Mile (FMLM), 2) Low Density, 3) Off-Peak, and 4) Paratransit. The typology also includes two MOD asset contribution models: 1) Agency-Operated MOD and 2) Agency-Subsidized Private MOD. Lessons learned for limiting competition with fixed-route public transit include: a) if agencies have sufficient resources, they can generally maintain greater data access and control over the service with Agency-Operated MOD than Agency-Subsidized Private MOD; b) public agencies can supplement the Agency-Operated MOD model with Agency-Subsidized Private MOD during peak demand; c) public agencies sometimes encourage FMLM transfers to fixed-route public transit by creating service zones that divide trip generators and attractors and assigning one or two designated transfer stops to each zone; and d) one approach to protecting fixed-route public transit is to restrict Low-Density MOD services to trips that start and end outside a geofenced fixed-route service area.

A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers

We propose a ridesharing strategy with integrated transit in which a private on-demand mobility service operator may drop off a passenger directly door-to-door, commit to dropping them at a transit station or picking up from a transit station, or to both pickup and drop off at two different stations with different vehicles. We study the effectiveness of online solution algorithms for this proposed strategy. Queueing-theoretic vehicle dispatch and idle vehicle relocation algorithms are customized for the problem. Several experiments are conducted first with a synthetic instance to design and test the effectiveness of this integrated solution method, the influence of different model parameters, and measure the benefit of such cooperation. Results suggest that rideshare vehicle travel time can drop by 40–60% consistently while passenger journey times can be reduced by 50–60% when demand is high. A case study of Long Island commuters to New York City (NYC) suggests having the proposed operating strategy can substantially cut user journey times and operating costs by up to 54% and 60% each for a range of 10–30 taxis initiated per zone. This result shows that there are settings where such service is highly warranted.

Investigating user perception on autonomous vehicle (AV) based mobility-on-demand (MOD) services in Singapore using the logit kernel approach

The rapid development of autonomous vehicles (AV) in recent years has drawn the attention of numerous countries in terms of its feasibility for use and deployment as individually-owned vehicles or for large-scale fleet planning and deployment as a mobility-on-demand (MOD) service. Singapore is no exception to this global trend and in her pursuit to be smart and car-lite, numerous efforts are made to have AV trials in place and test out their potential deployment in the city state. As one of the many prerequisites of AV planning, public perception on AV plays a vital role when designing any potential AV deployment scheme. As such, a stated preference survey comprising both online survey and field interviews/surveys, was performed island-wide to understand how commuters in Singapore perceive about different AV-based MOD modes. The logit kernel model is adopted to determine how different preference attributes and key demographic indicators can affect the use of AV-based MOD services over other existing first- and last-mile connection modes. The model results have identified how demographics such as gender, age, housing type, education level and income level can influence the travel mode choice. Also, the impacts brought by individuals’ stated preferences over convenience, privacy and familiarity of ride-hailing apps are also investigated. Such findings can provide useful insight in planning future car-lite towns and implementing AV-based MOD services in these towns.