A framework to integrate mode choice in the design of mobility-on-demand systems

Mobility-on-Demand (MoD) systems are generally designed and analyzed for a fixed and exogenous demand, but such frameworks fail to answer questions about the impact of these services on the urban transportation system, such as the effect of induced demand and the implications for transit ridership. In this study, we propose a unified framework to design, optimize and analyze MoD operations within a multimodal transportation system where the demand for a travel mode is a function of its level of service. An application of Bayesian optimization (BO) to derive the optimal supply-side MoD parameters (e.g., fleet size and fare) is also illustrated. The proposed framework is calibrated using the taxi demand data in Manhattan, New York. Travel demand is served by public transit and MoD services of varying passenger capacities (1, 4 and 10), and passengers are predicted to choose travel modes according to a mode choice model. This choice model is estimated using stated preference data collected in New York City. The convergence of the multimodal supply-demand system and the superiority of the BO-based optimization method over earlier approaches are established through numerical experiments. We finally consider a policy intervention where the government imposes a tax on the ride-hailing service and illustrate how the proposed framework can quantify the pros and cons of such policies for different stakeholders

What is the market potential for on-demand services as a train station access mode?

Ride-hailing and other on-demand mobility services are often proposed as a solution for improving the accessibility of public transport by offering first/last mile connectivity. We study the potential of using on-demand services to improve train station access by means of a three-step sequential stated preference survey. We compare the preferences for on-demand services with the bicycle, car and public transport for accessing two alternative train stations at different access distances. We estimate a joint access mode and train station choice model. By estimating a latent class choice model, we uncover five distinct segments in the population. We describe the classes based on their stated preferences, travel behaviour, attitudes towards new mobility and their socio-demographic characteristics. The two largest classes, accounting for over half of the sample, are the most likely to adopt on-demand services. Having an average willingness-to-pay, they would choose these services for longer access distances, competing mainly with the car and local public transport. Applying the model estimates, we observe that while on-demand services mainly compete with public transportation (obtaining most of its travellers from it), they are not able to fully substitute a public transport service for train station access, as many users would switch to cycling or driving a car, rather than opting for the on-demand service

Metrics for Quantifying Shareability in Transportation Networks: The Maximum Network Flow Overlap Problem

Cities around the world vary in terms of their transportation networks and travel demand patterns; these variations affect the viability of shared mobility services. This study proposes metrics to quantify the shareability of person-trips in a city, as a function of two inputs--the road network structure and origin-destination (OD) travel demand. The study first conceptualizes a fundamental shareability unit, 'flow overlap'. Flow overlap denotes, for a person-trip traversing a given path, the weighted (by link distance) average number of other trips sharing the links along the original person's path. The study extends this concept to the network level and formulates the Maximum Network Flow Overlap Problem (MNFLOP) to assign all OD trips to paths that maximize network-wide flow overlap. The study utilizes the MNFLOP output to calculate metrics of shareability at various levels of aggregation: person-trip level, OD level, origin or destination level, network level, and link level. The study applies the MNFLOP and associated shareability metrics to different OD demand scenarios in the Sioux Falls network. The computational results verify that (i) MNFLOP assigns person-trips to paths such that flow overlaps significantly increase relative to shortest path assignment, (ii) MNFLOP and its associated shareability metrics can meaningfully differentiate between different OD trip matrices in terms of shareability, and (iii) an MNFLOP-based metric can quantify demand dispersion--a metric of the directionality of demand--in addition to the magnitude of demand, for trips originating or terminating from a single node in the network. The paper also includes an extensive discussion of potential future uses of the MNFLOP and its associated shareability metrics

Lane change decision prediction:an efficient BO-XGB modelling approach with SHAP analysis

The lane-change decision (LCD) is a critical aspect of driving behaviour. This study proposes an LCD model based on a Bayesian optimization (BO) framework and extreme gradient boosting (XGBoost) to predict whether a vehicle should change lanes. First, an LCD point extraction method is proposed to refine the exact LCD points with a highD dataset to increase model learning accuracy. Subsequently, an efficient XGBoost with BO (BO-XGB) was used to learn the LCD principles. The prediction accuracy on the highD dataset was 99.14% with a computation time of 66.837s. The accuracy on the CQSkyEyeX dataset was 99.45%. Model explanation using the shapley additive explanation (SHAP) method was developed to analyse the mechanism of the BO-XGB’s LCD prediction results, including global and sample explanations. The former indicates the particular contribution of each feature to the model prediction throughout the entire dataset. The latter denotes each feature's contribution to a single sample