On-demand Mobility Research Articles

Ride matching and vehicle routing for on-demand mobility services

Ride matching and vehicle routing for on-demand mobility services

Re-shaping urban mobility – Key to Europe´s green transition

This paper outlines the vision of EIT Urban Mobility towards sustainable urban mobility. EIT Urban Mobility is an initiative of the European Institute for Innovation and Technology (EIT), a body of the European Union. EIT Urban Mobility's ecosystem counts more than 260 organisations from cities, research & academia, and industry working to enable people and goods to move affordable, fast, comfortably, safely, and cleanly.In the context of climate emergency and extreme weather events that an increasing number of European cities are already facing, it is of utmost importance to develop, and scale decarbonised urban mobility solutions. Such solutions must address acute challenges faced by cities and their inhabitants, linking urban and mobility planning, while actively engaging with citizens at all stages of transformation processes, from design to implementation.Our vision aims to facilitate this process by channelling public and private efforts towards priority areas of transformation, which are technological and behavioural pathways leading to more sustainable urban mobility spanning from street experiments to connected and shared on-demand mobility. The paper also identifies structural enablers of change, which are key technological and regulatory innovations required to turn our vision into an everyday reality.

Assessing the Implementation of On-Demand Transportation Services for People with Disabilities

As there are many barriers to the effective operation of paratransit services required by the Americans with Disabilities Act (ADA), both riders and transit agencies have incentives to shift some paratransit ridership to more effective transit services. To address these barriers, transit agencies have started partnering with technology-enabled third-party companies (e.g., transportation network companies, taxi companies) to provide new on-demand mobility options for people with disabilities. Previous studies identified the benefits and challenges of implementing on-demand services through a public-private partnership, but did not fully investigate the implementation of this emerging on-demand service from the perspective of serving individuals with disabilities. This study conducted online surveys and semi-structured interviews with 10 transit agencies across the United States to understand how this new on-demand mobility option has complemented ADA paratransit services, and what challenges and outcomes the partnerships have experienced over time. Findings indicate that despite the positive impact of implementing the on-demand service on overall service operation and delivery, transit agencies still experienced challenges in providing accessible and reliable on-demand services for riders. Transit agencies and partnering mobility service companies should work together to increase technology adoption, availability and reliability of wheelchair accessible services, and driver sensitivity to the needs of individuals with disabilities, as well as to support the sustainability of the program through continuous evaluation.

A joint behavioral choice model for adoption of automated vehicle ride sourcing and carsharing technologies: Role of built environment & sustainable travel behaviors

Rapid advancements in vehicle automation and a shift towards collaborative consumption trends will disrupt future urban mobility systems. We present a joint model of consumers’ affinity towards shared automated vehicles (SAVs) with two distinct yet related configurations: automated vehicle (AV) carsharing and AV ride sourcing. Compact and walkable built environment is known to support active travel and shared mobility but the potential impacts of built environment on adoption of automated shared mobility are largely unknown. Besides sociodemographic, sustainable travel, and technology-related factors, we examine the role of neighborhood-level walkability (composite measure of land-use, pedestrian-oriented urban design, and transit accessibility) in shaping public’s opinion about AV carsharing and AV ride sourcing technologies. Detailed travel behavior and stated preference/taste data for over 2700 individuals from the most recent 2019 Puget Sound Travel Survey are complemented with neighborhood-level high-resolution data on built environment fabric. Two dependent variables of interest include individual’s level of interest in AV carsharing and AV ride sourcing programs. A novel and comprehensive geo-additive heterogeneous copula-based joint behavioral model is developed - revealing complex stochastic dependence patterns between users’ interest in AV ride sourcing and carsharing programs and different layers of systematic and random taste heterogeneity. Results show that improving neighborhood walkability can be an effective way to increase public’s interest in using automated ride sourcing and carsharing services. Findings also highlight the opportunity to enable SAV adoption by harnessing the synergies between green (transit) travel, technology use for work/non-work purposes, use of existing mobility-on-demand (MOD) services, and affinity towards automated MOD services. Results are translated to potential policies for designing proactive behavioral and built environment interventions for accelerating SAV adoption with an enhanced sensitivity towards the needs of distinct and more vulnerable population groups (e.g., older, unemployed, and/or less educated individuals). The study methodologically contributes by presenting a comprehensive behavioral model for SAV adoption by differentiating ride sourcing and carsharing.

Smart Mobility for Rural Areas: On-demand Mobility

Smart Mobility for Rural Areas: On-demand Mobility

Demand Exploration of Automated Mobility On-Demand Services Using an Innovative Simulation Tool

The prospect of automated mobility on-demand (AMoD) services in urban areas has highlighted critical challenges surrounding the sustainable development of urban mobility. To address this gap, we created an improved simulation tool by combining SimMobility’s demand simulator with the hybrid meso-micro supply model of Aimsun Next. A realistic and city-scale examination of AMoD using such an advanced simulation tool can address the limitations identified in the literature and significantly promote the understanding of AMoD services. In this paper, we demonstrate the use of our improved simulation tool and focus on demand exploration of AMoD services in the Tel-Aviv metropolitan area. We employ an activity-and agent-based framework, for both single and shared AMoD rides, and explore 6 service cost scenarios and its impact on demand elasticities, mode choice, travel patterns and AMoD use by population groups. Our results indicate that there is no existing latent demand in Tel-Aviv metropolitan area and the extent of mode shifts from active modes and public transportation to AMoD is neglectable. This is due to AMoD services average travel costs, which is high as compared to all other modes, even with the largest fare reduction examined. Furthermore, it was found that AMoD demand, as a single service, is more elastic than when AMoD is shared, as cost elasticities drops as fare reduction increases. Unlike other modes of transportation, the maximum number of AMoD trips is obtained for trips between 10 to 20 kilometers, while young riders and full-time students are responsible for most of AMoD trips.

Modeling the Competition between Multiple Automated Mobility On-Demand Operators: An Agent-Based Approach

Modeling the Competition between Multiple Automated Mobility On-Demand Operators: An Agent-Based Approach

A Big Data Analysis on Urban Mobility: Case of Bangkok

Designing an efficient on-demand mobility service requires comprehensive knowledge of the statistical characteristics of trips. In other words, it is critical to know how long passengers typically spend on a trip and how far they usually travel. Likewise, it is important to learn how much time a driver spends searching for passengers. This study presents a statistical analysis of taxi trips in Bangkok based on real traces of 5,853 taxis over the period of three months. Significant insights on trip volume, trip time, trip distance, and origin-destination distance are derived. In addition, the probability distributions of trip time, trip distance, and origin-destination distance are also characterized based on two goodness-of-fit tests. To our knowledge, this characterization is done for the first time for Bangkok taxi trips. It is shown that a lognormal distribution can best describe the empirical trip time distribution. On the other hand, a Weibull distribution can best describe the empirical trip distance distribution and the empirical origin-destination distance distribution. These distributions are essential to traffic simulation. Finally, the efficiency of the Bangkok taxi system is also quantified both at the system level and at the agent level.

Deep Neural Network for On-Demand Mobility Mode Choice Analysis: Issues on Adaptability and Interpretability

Deep Neural Network for On-Demand Mobility Mode Choice Analysis: Issues on Adaptability and Interpretability

Investigating Commuter Preferences for On-Demand Mobility Services Considering Working Flexibility: A Hybrid Choice Modeling Approach

Investigating Commuter Preferences for On-Demand Mobility Services Considering Working Flexibility: A Hybrid Choice Modeling Approach

Taxonomy and Abstraction of the On-Demand Mobility Aviation System for Smart Cities

Taxonomy and Abstraction of the On-Demand Mobility Aviation System for Smart Cities

Methodological Distribution of Virtual Stops for Ridepooling

In recent years, an increasing number of shared and on-demand mobility services were deployed in several cities. Most of these services utilize virtual stops as pick-up and drop-off points due to a better service efficiency compared to a door-to-door operating policy. Virtual stops do not include any physical element of a conventional bus stop. Therefore, they are only visible on a mobile device with the proper application. Emerging from the flexibility of distributing virtual stops, strategic considerations are essential to optimize the service quality and usability. This work introduces three approaches to identify locations for virtual stops, followed by methodological strategies. Different characteristics are obtained by applying these methods, assessed by a comprehensive utility analysis regarding users’ and providers’ perspectives. The evaluation shows that for users, virtual stops located at streetlamps are the most advantageous. In contrast, providers benefit from virtual stops at intersections, mainly due to better routing efficiency.

Near-on-demand mobility. The benefits of user flexibility for ride-pooling services

Mobility-On-Demand (MoD) services have been transforming the urban mobility ecosystem. However, they raise a lot of concerns for their impact on congestion, Vehicle Miles Traveled (VMT), and competition with transit. There are also questions about their long-term survival because of inherent inefficiencies in their operations. Considering the popularity of the MoD services, increasing ride-pooling is an important means to address these concerns. Shareability depends not only on riders’ attitudes and preferences but also on operating models deployed by providers. The paper introduces an advance requests operating model for ride pooling, users may request rides in advance of their desired departure times. A platform with efficient algorithms for request matching, vehicle routing, rebalancing, and flexible user preferences is developed. Data from Chengdu, China, and New York, United States are used to evaluate the performance of the advance requests system. The impact of various design aspects of the system (e.g. advance requests horizon, vehicle capacity) on its performance is investigated. The sensitivity of the results to user preferences in terms of the level of service (time to be served and excess trip time), willingness to share and place requests in advance, and traffic conditions are explored. The results suggest that significant benefits in terms of sustainability, level of service, and fleet utilization can be realized when advance requests are along with an increased willingness to share. Furthermore, even “near-on-demand” (relative short planning horizons) operations can offer benefits for all stakeholders (users, operators, cities).

The Impact of Fleet Coordination on Taxi Operations

On-demand mobility has existed for more than 100 years in the form of taxi systems. Comparatively recently, ride-hailing schemes have also grown to a significant mode share. Most types of such one-way mobility-on-demand systems allow drivers taking independent decisions. These systems are not or only partially coordinated. In a different operating mode, all decisions are coordinated by the operator, allowing for the optimization of certain metrics. Such a coordinated operation is also implied if human-driven vehicles are replaced by self-driving cars. This work quantifies the service quality and efficiency improvements resulting from the coordination of taxi fleets. Results based on high-fidelity transportation simulations and data sets of existing taxi systems are presented for the cities of San Francisco, Chicago, and Zurich. They show that fleet coordination can strongly improve the efficiency and service level of existing systems. Depending on the operator and the city’s preferences, empty vehicle distance driven and fleet sizes could be substantially reduced, or the wait times could be reduced while maintaining the current fleet sizes. The study provides clear evidence that full fleet coordination should be implemented in existing mobility-on-demand systems, even before the availability of self-driving cars.

On the scalability of private and pooled on-demand services for urban mobility in Amsterdam

ABSTRACT The emergence of on-demand transport services is set to change the mobility landscape in urban areas. This study investigates the potential scalability of an on-demand mobility system to substitute motorised trips performed by private cars and public transport in Amsterdam, The Netherlands. We adopt an agent-based simulation framework and investigate scenarios where either private and pooled on-demand services replace private car trips, public transport trips, or both private car and public transport trips. Service performance in terms of level of service offered and operational efficiency are analysed. Results indicate that pooled on-demand services fare better than private on-demand in terms of veh-km travelled and the empty drive ratio. Private on-demand services generate 43%, 38%, and 44% more veh-km than pooled on-demand services when on-demand services replace car trips, public transport trips, or car and public transport trips, respectively. However, private on-demand services offer shorter total travel times than pooled on-demand for all scenarios.

Mobility-on-demand pricing versus private vehicle TCO: how cost structures hinder the dethroning of the car

This study uses a unique dataset on the cost of motoring in Germany to analyse cost competitiveness of emerging mobility-on-demand (MOD) services. Previous studies have focused on comparing current and projected MOD prices with the average cost of private motoring. This study quantifies which proportion of private car travel would actually turn out to be costlier than MOD given that MOD costs drop below certain levels relative to the cost of private motoring. In this context, not the average but the distribution of the costs of motoring are the key issue. These costs are strongly skewed across the cars in private households when including new and old vehicles: a large proportion of private car kilometres are driven at relatively low cost. The study uses simplified scenario settings with MOD price levels ranging from 0.1 €/km to 1.5 €/km to make predictions of hypothetical modal shifts under the assumption that car user switch to the most economic mode of travel. These modal shifts serve as an indicator of MOD cost competitiveness. The results indicate that MOD prices would have to drop to 0.5 €/km or lower to have a notable impact on use of the private car if cost was the key mode choice criterion. Only if MOD prices drop down to a level of about 0.3 €/km—quite possibly a lower boundary for automated MOD—MOD-enabled mobility packages would be the less costly alternative to the private car for a substantial proportion of mileage. However, even at that MOD price level, the private car would still be the most economic option for the majority of today’s car user kilometres. Our findings illustrate that the skewed distribution of the cost of owning and running private cars—where many of those who drive much drive inexpensively—substantially dampens the disruptive potential of MOD. While we use data from Germany to illustrate this, many of our findings are more widely applicable.

Interpretable data-driven demand modelling for on-demand transit services

In recent years, with the advancements in information and communication technology, different emerging on-demand shared mobility services have been introduced as innovative solutions in the low-density areas, including on-demand transit (ODT), mobility on-demand (MOD) transit, and crowdsourced mobility services. However, due to their infancy, there is a strong need to understand and model the demand for these services. In this study, we developed trip production and distribution models for ODT services at Dissemination areas (DA) level using four machine learning algorithms: Random Forest (RF), Bagging, Artificial Neural Network (ANN) and Deep Neural Network (DNN). The data used in the modelling process were acquired from Belleville’s ODT operational data and 2016 census data. Bayesian optimalization approach was used to find the optimal architecture of the adopted algorithms. Moreover, post-hoc model was employed to interpret the predictions and examine the importance of the explanatory variables. The results showed that the land-use type was the most important variable in the trip production model. On the other hand, the demographic characteristics of the trip destination were the most important variables in the trip distribution model. Moreover, the results revealed that higher trip distribution levels are expected between dissemination areas with commercial/industrial land-use type and dissemination areas with high-density residential land-use. Our findings suggest that the performance of ODT services can be further enhanced by (a) locating idle vehicles in the neighbourhoods with commercial/industrial land-use and (b) using the spatio-temporal demand models obtained in this work to continuously update the operating fleet size.

Impacts of autonomous on-demand mobility service: A simulation experiment in the City of Athens

ABSTRACT High-capacity Connected and Autonomous Vehicles (CAVs) are expected to be extensively utilized by on-demand services. This paper aims to assess the impacts of large-scale autonomous on-demand mobility services on traffic, environment, and road safety, under various service specifications using microsimulation. To that end, an urban on-demand shuttle service was designed, optimized, based on a variation of the Dial-a-Ride optimization problem (DARP), and implemented in the road network of the city of Athens to serve different portions of demand with various capacity specifications. It was then investigated through forty mobility scenarios, with differences in policy implementation and market penetration rate of CAVs. Findings show that it led to improved network level traffic conditions, as delays decreased, and that traffic impacts evolve with fleet capacity and served demand. Furthermore, the number of conflicts decreased and the environmental conditions significantly improved, with CAVs in the network, while the traveled distance increased.

A Simulation-Based Evaluation of a Cargo-Hitching Service for E-Commerce Using Mobility-on-Demand Vehicles

Time-sensitive parcel deliveries—shipments requested for delivery in a day or less—are an increasingly important aspect of urban logistics. It is challenging to deal with these deliveries from a carrier perspective. These require additional planning constraints, preventing the efficient consolidation of deliveries that is possible when demand is well known in advance. Furthermore, such time-sensitive deliveries are requested to a wider spatial scope than retail centers, including homes and offices. Therefore, an increase in such deliveries is considered to exacerbate negative externalities, such as congestion and emissions. One of the solutions is to leverage spare capacity in passenger transport modes. This concept is often denominated as cargo hitching. While there are various system designs, it is crucial that such a solution does not deteriorate the quality of service of passenger trips. This research aims to evaluate the use of mobility-on-demand (MOD) services that perform same-day parcel deliveries. To test the MOD-based solutions, we utilize a high-resolution agent- and activity-based simulation platform of passenger and freight flows. E-commerce demand carrier data collected in Singapore are used to characterize simulated parcel delivery demand. We explore operational scenarios that aim to minimize the adverse effects of fulfilling deliveries with MOD service vehicles on passenger flows. Adverse effects are measured in fulfillment, wait, and travel times. A case study on Singapore indicates that the MOD services have potential to fulfill a considerable amount of parcel deliveries and decrease freight vehicle traffic and total vehicle kilometers travelled without compromising the quality of MOD for passenger travel. Insights into the operational performance of the cargo-hitching service are also provided.

Local Climate Action Planning as a Tool to Harness Greenhouse Gas Emissions Mitigation and the Equity Potential of Autonomous Vehicles and On-Demand Mobility

This paper focuses on how cities can use climate action plans (CAPs) to ensure that on-demand mobility and autonomous vehicles (AVs) help reduce, rather than increase, greenhouse gas (GHG) emissions and inequitable impacts from the transportation system. We employed a three-pronged research strategy involving: (1) an analysis of the current literature on on-demand mobility and AVs; (2) a systematic content analysis of 23 CAPs and general plans (GPs) developed by municipalities in California; and (3) a comparison of findings from the literature and content analysis of plans to identify opportunities for GHG emissions reduction and mobility equity. Findings indicate that policy and planning discussions should consider the synergies between AVs and on-demand mobility as two closely related emerging mobility trends, as well as the key factors (e.g., vehicle electrification, fuel efficiency, use and ownership, access, and distribution, etc.) that determine whether the deployment of AVs would help reduce GHG emissions from transportation. Additionally, AVs and on-demand mobility have the potential to contribute to a more equitable transportation system by improving independence and quality of life for individuals with disabilities and the elderly, enhancing access to transit, and helping alleviate the geographic gap in public transportation services. Although many municipal CAPs and GPs in California have adopted several strategies and programs relevant to AVs and on-demand mobility, several untapped opportunities exist to harness the GHG emissions reduction and social benefits potential of AVs and on-demand mobility.