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

Shared Autonomous Mobility Services Show Promise for Increasing Access to Employment in Southern California

Workers in Southern California currently face transportationrelated challenges accessing employment opportunities, including but not limited to high parking costs and/or limited parking availability in dense employment and residential areas; long commute distances between residential areas and employment opportunities; and poor transit service quality in many areas. These challenges are particularly burdensome for low-income households that may not have access to a personal vehicle and/or live in jobpoor neighborhoods, as having a personal vehicle may be the only viable way to get to work

Assessment of the Employment Accessibility Benefits of Shared Autonomous Mobility Services

The goal of this study is to assess and quantify the potential employment accessibility benefits of Shared Autonomous Mobility Service (SAMS) commute modes across a large diverse metropolitan region considering heterogeneity in the working population. To meet this goal, this study employs 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. This research further captures heterogeneity of workers using latent class analysis (LCA). The LCA model inputs include the socio-demographic characteristics of workers to subsequently account for different worker clusters valuing different types of employment opportunities differently. The accessibility analysis results 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

Dynamic Modeling and Real-time Management of a System of EV Fast-charging Stations

Demand for electric vehicles (EVs), and thus EV charging, has steadily increased over the last decade. However, there is limited fast-charging infrastructure in most parts of the world to support EV travel, especially long-distance trips. The goal of this study is to develop a stochastic dynamic simulation modeling framework of a regional system of EV fast-charging stations for real-time management and strategic planning (i.e., capacity allocation) purposes. To model EV user behavior, specifically fast-charging station choices, the framework incorporates a multinomial logit station choice model that considers charging prices, expected wait times, and detour distances. To capture the dynamics of supply and demand at each fast-charging station, the framework incorporates a multi-server queueing model in the simulation. The study assumes that multiple fast-charging stations are managed by a single entity and that the demand for these stations are interrelated. To manage the system of stations, the study proposes and tests dynamic demand-responsive price adjustment (DDRPA) schemes based on station queue lengths. The study applies the modeling framework to a system of EV fast-charging stations in Southern California. The results indicate that DDRPA strategies are an effective mechanism to balance charging demand across fast-charging stations. Specifically, compared to the no DDRPA scheme case, the quadratic DDRPA scheme reduces average wait time by 26%, increases charging station revenue (and user costs) by 5.8%, while, most importantly, increasing social welfare by 2.7% in the base scenario. Moreover, the study also illustrates that the modeling framework can evaluate the allocation of EV fast-charging station capacity, to identify stations that require additional chargers and areas that would benefit from additional fast-charging stations

Non-myopic Pathfinding for Shared-Ride Vehicles: A Bi-Criteria Best-Path Approach Considering Travel Time and Proximity to Demand [Research Brief]

USDOT Grant 69A3551747109Caltrans contract 65A06The overarching goal of this research project is to improve the operational efficiency of shared-ride mobility-on-demand services (SRMoDS) like UberPool and Lyft Line, in order to increase vehicle occupancies and decrease vehicle mileage. To meet this goal, the objective of this study is to develop a network pathfinding algorithm that considers both a network path\u2019s travel time and its proximity to potential future demand (i.e., travel requests), as opposed to a conventional shortest path algorithm that solely considers travel time

Quantifying Sharing Potential in Transportation Networks and the Benefits of Mobility-on-Demand Services with Virtual Stops

Cities around the world vary in terms of their transportation network structure and travel demand patterns, with implications for the viability of shared mobility services. Recently, the urban mobility sector has witnessed a significant transformation with the introduction of several new types of Mobility-on-Demand (MOD) services that vary in terms of their capacity and flexibility of routes, schedules, and user Pickup and Dropoff (PUDO) locations. This dissertation proposes models and algorithms to analyze sharing in transportation networks and Mobility-on-Demand (MOD) services in two comprehensive studies. The first study aims to quantify the sharing potential of travelers within a city or region’s transportation network. The second study aims to measure trade-offs in user and operator costs when MOD services operate with Virtual Stops which refer to flexible PUDO locations requiring travelers to walk the first/last mile of their trip.The first study addresses the lack of metrics that jointly characterize a region’s travel demand patterns and its transportation network in terms of the potential for travelers to share trips. I define sharing potential in the form of person-trip shareability and introduce and conceptualize ‘flow overlap’ as the fundamental metric to capture shareability. The study formulates the Maximum Network Flow Overlap Problem (MNFLOP), a math program that assigns person-trips to network paths that maximize network-wide flow overlap. The results reveal that the shareability metrics can (i) meaningfully differentiate between different Origin-Destination trip matrices in terms of flow overlap, and (ii) quantify demand dispersion of trips from a single location considering the underlying road network. Finally, I validate MNFLOP’s ability to quantify shareability by showing that demand patterns with higher flow overlap are strongly associated with lower mileage routes for a last-mile microtransit service. The second study proposes a scalable algorithm for operating shared-ride MOD services with flexible and dynamic PUDO locations—called C2C (Corner-to-Corner) services—in a congestible network. I compare four MOD service types: Door-to-Door (D2D) Ride-hailing, D2D Ride-pooling, C2C Ride-hailing, and C2C Ride-pooling by evaluating operator and user costs. The results show that Ride-pooling reduces operator costs while slightly increasing user costs, whereas C2C reduces operator costs but significantly increases user costs. Combining Ride-pooling and C2C appears promising to reduce operator costs and to reduce vehicles miles traveled (VMT) in MOD systems

Shared Autonomous Mobility Services Show Promise for Increasing Access to Employment in Southern California

Workers in Southern California currently face transportationrelated challenges accessing employment opportunities, including but not limited to high parking costs and/or limited parking availability in dense employment and residential areas; long commute distances between residential areas and employment opportunities; and poor transit service quality in many areas. These challenges are particularly burdensome for low-income households that may not have access to a personal vehicle and/or live in jobpoor neighborhoods, as having a personal vehicle may be the only viable way to get to work