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

Quantifying the employment accessibility benefits of shared automated vehicle mobility services: Consumer welfare approach using logsums

The goal of this study is to assess and quantify the potential employment accessibility benefits of shared-use automated vehicle (AV) mobility service (SAMS) modes across a large diverse metropolitan region considering heterogeneity in the working population. To meet this goal, this study proposes employing a welfare-based (i.e. logsum-based) measure of accessibility, obtained via estimating a hierarchical work destination-commute mode choice model. The employment accessibility logsum measure incorporates the spatial distribution of worker residences and employment opportunities, the attributes of the available commute modes, and the characteristics of individual workers. The study further captures heterogeneity of workers using a latent class analysis (LCA) approach to account for different worker clusters valuing different types of employment opportunities differently, in which the socio-demographic characteristics of workers are the LCA model inputs. The accessibility analysis results in Southern California indicate: (i) the accessibility benefit differences across latent classes are modest but young workers and low-income workers do see higher benefits than high- and middle-income workers; (ii) there are substantial spatial differences in accessibility benefits with workers living in lower density areas benefiting more than workers living in high-density areas; (iii) nearly all the accessibility benefits come from the SAMS-only mode as opposed to the SAMS+Transit mode; and (iv) the SAMS cost per mile assumption significantly impacts the magnitude of the overall employment accessibility benefits

Effective and Efficient Fleet Dispatching Strategies for Dynamically Matching AVs to Travelers in Large-scale Transportation Systems

This paper addresses the problem of dynamically matching automated vehicles (AVs) to open traveler requests in a large-scale automated-mobility-on-demand (AMOD) simulation framework. While optimization-based matching strategies based on the linear assignment problem formulation significantly outperform simple heuristic strategies (e.g. nearest neighbor), the scalability of the assignment problem limits its applicability to large problem instances. This study proposes a fleet dispatching strategy to dynamically assign AVs to travelers that involves the assignment problem formulation but restricts the decision space to reduce computational time. First, we significantly trim the decision space via only considering the k-closest open requests around each idle vehicle or k-closest idle vehicles around each open request. Second, we only calculate point-to-point shortest paths for vehicles and travelers that are close in spatial proximity. For vehicles and travelers that are not close in proximity, we use zone-to-zone travel time estimates. This study embeds the proposed AV fleet dispatching strategy within Polaris-an agent-based transportation simulation modeling framework. Within Polaris, the restricted fleet dispatching strategy proposed in this significantly outperforms (i) existing large-scale strategies in terms of fleet performance and (ii) the unrestricted assignment problem strategy in terms of computational performance