Evaluating Automated Demand Responsive Transit Using Microsimulation

Abstract

Recent advancements in automated vehicle technology and the concurrent emergence of ride-hailing services have focused increasing attention on Automated Mobility-on-Demand (AMOD; a system of shared driverless taxis) as a potential solution for sustainable future urban mobility. However, the impacts of an unrestricted deployment of AMOD are as yet uncertain and likely to be contextspecific; evidence with existing on-demand services suggests that they may lead to the cannibalization of mass-transit and increased traffic congestion. In this context, automated demand-responsive transit (also termed microtransit), which provides similar on-demand services (stop-to-stop or curbside) through higher capacity vehicles, may prove to be a promising substitute and/or complement. In this study, we evaluate the performance of such an automated demand response transit system (hereafter AMOD minibus) through agent-based simulations of the Singapore network. Towards this end, we extend SimMobility (an agentand activity-based microsimulation laboratory) with the capability of modeling an AMOD minibus service including demand, supply and their interactions. On the demand side, we use an activity-based model system that draws on data from a stated-preferences survey conducted in Singapore. On the supply side, an insertion heuristic is applied to dynamically perform both the assignment of requests to vehicles and vehicle routing. Scenario simulations on the Singapore network (with an area-wide deployment of the AMOD services) indicate the potential benefits of an automated demand responsive transit service for local circulation, which can result in a reduction of Vehicle Kilometres Traveled of up to 50% (compared to the AMOD shared taxis) whilst satisfying the same demand, with a modest increase in average travel times. INDEX TERMS Agent-based simulation, automated mobility-on-demand (AMOD), high-capacity ridesharing, shareability.

Identifying the Potential for Partial Integration of Private and Public Transportation

“…Lastly, extensions of the current work could consider other form factors, such as medium sized (i.e., 6-10 passenger) vehicles as alternatives to both taxis and buses and evaluate the total impact in terms of congestion, energy use, passenger comfort and convenience. We note that previous work in this area was limited to using taxi trips as an estimate of demand [21,47]; our results show the importance of including the complete picture of urban transportation demand, i.e., both public and private transportation users. Furthermore, while our work shows a potential for matching trips, any such service will face challenges in implementing user interaction solutions that can be conveniently used without excluding groups of users, e.g.…”

“…Extensions of the current work could look into incorporating predicted demand in presumed future scenarios of regulatory and pricing environment and mode choice models that incorporate new mobility modes [47]. This will allow a more comprehensive evaluation of benefits, and inform about strategies that governments can employ to ensure sustainable urban transportation in the age of autonomous driving.…”

“…This will allow a more comprehensive evaluation of benefits, and inform about strategies that governments can employ to ensure sustainable urban transportation in the age of autonomous driving. Furthermore, incorporating a more detailed model of passenger choices can account for how differences in trip purpose affect the value of time and willingness to take part of such shared transportation options [47]; this in turn will inform on how the implementation of opportunistic ride-sharing can be optimized to cater more closely to the subset of public transportation users who are most eager to use it, and where maximal benefits can be realized [1,39].…”

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

“…For the matching of requests to vehicles, an insertion heuristic is used, which maintains a schedule for each vehicle and attempts to insert incoming requests into the existing schedules of nearby vehicles within a pre-specified search radius equal to 5 km, to ensure that waiting times and travel times of all passengers are within pre-defined thresholds (15 min for maximum wait/travel times). More details of the matching heuristic may be found in [39,41,42]. Minor modifications are made to the matching heuristic to model the cargo-hitching service.…”

Integrating Passenger Incentives to Optimize Routing for Demand-Responsive Customized Bus Systems

“…With the recent development and application of mobile internet and shared economy, the service modes of DRT have become various and are more competitive to conventional fixed-route bus transit [5]. This alternative mode is also the most promising form to embrace the implementation and application of autonomous vehicles [6].…”