conference paper
published journal article
Approximately 1 million Californian households do not own a motor vehicle (hereafter a â??carâ??). These households, who are often forgotten in transportation policy discussions, can be organized into two groups based on whether they are voluntarily carless or not. Understanding why some households decide to voluntarily forgo cars could inform policies aiming at reducing dependency on cars. Understanding the plight of households who are not able to own a car is no less important as these households are at greater risk of social exclusion. Unfortunately, the authors’ knowledge of carless households is still sketchy so the purpose of this paper is to start filling this gap. They analyze data from the 2012 California Household Travel Survey using univariate tests and Generalized Structural Equation Models (GSEM) that account for residential self-selection to assess the impacts of various socio-economic and built environment variables on the likelihood to be carless, voluntarily or not. The authors’ results (GSEM Model 1) indicate that carless households are more likely to have less education, a lower income, and a smaller number of members than motorized families. They also tend to live in denser, more land-use diverse, and more walkable areas with better transit coverage. Contrasting voluntarily and involuntarily carless households (GSEM Model 3), the authors find that involuntarily carless households are less affluent on average and they tend to live in areas that are less land-use diverse, less walkable, and with worse transit coverage. Finally, although residential self-selection is present, its impacts are minor.
conference paper |
Jan 2018
published journal article |
Nov 2019
published journal article |
Dec 2020
policy brief |
May 2020
research report |
May 2018
published journal article
Little appears to be known about the capitalization of transportation accessibility in South Asian housing markets, which typically differ from those of industrialized countries. This study starts addressing this gap by providing empirical evidence about the nature and the magnitude of the value of accessibility as reflected by residential rents in Rajshahi City, Bangladesh. Results of the authors’ spatial auto-regressive disturbance (SARAR) spatial hedonic model estimated on 526 observations from a random sample collected via in-person interviews indicate that the rent of a multi-unit dwelling decreases by 0.0239% for every 1% increase in network access distance to the nearest major road. Moreover, proximity (within 400 m) to a primary school and to a healthcare facility commands rent premiums of respectively 93.55 BDT ($1.40) and 109.45 BDT ($1.64). Surprisingly, whether access roads are paved or not does not statistically impact rents, probably because of the dominance of walking, rickshaws use, and biking, combined with the rarity of personal cars. Likewise, proximity to bus stops and to train stations is not reflected in rents of multi-family dwellings, likely because buses and trains in Rajshahi City only provide regional and national service. Differences in estimates of the authors’ spatial models between maximum likelihood (ML) and generalized spatial two-stage-least-squares illustrate the danger of relying on ML in the presence of heteroskedasticity. These results should be useful for planning transportation infrastructure funding measures in least developed country cities like Rajshahi City.
Preprint Journal Article
Cities around the world vary in terms of their urban forms, transportation networks, and travel demand patterns; these variations affect opportunities for travelers to share trips, and the viability of shared mobility services. This study proposes metrics to quantify the maximum shareability of person-trips in a city, or region of a city, as a function of two inputs—the transportation 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 person-trips sharing the links along the original person-trip’s path. The study extends this concept to the network level and formulates the Maximum Network Flow Overlap Problem (MNFLOP) to assign all OD person-trips to network paths that maximize flow overlap in the whole network. The study also proposes an MNFLOP variant with a second objective function term, detour distance, to capture the trade-off between minimizing travel distance and maximizing shareability. 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 maximum 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/location in the network. The paper also includes an extensive discussion of potential future uses of the MNFLOP and its associated shareability metrics.
policy brief
published journal article |
Dec 2023
research report |
Jan 2022
published journal article
research report
This paper examines two green driving strategies based on inter-vehicle communication (IVC) aimed at reducing tailpipe emissions. Excessive speed and stop-and-go traffic significantly increase fuel consumption and vehicle emissions. It is anticipated that IVC, including vehicle-to-vehicle and vehicle-to-infrastructure communications, will be available in the future to relay time-critical and location-based traffic information between vehicles allowing people to drive more smoothly and safely, which also affects fuel use. In this paper the authors study green driving strategies based on IVC and their effects on emission reductions in different traffic conditions. Newellâ??s car-following model and VT-Micro are integrated to establish a simulation platform. With higher market penetration of IVC-equipped vehicles, reduction of emissions and fuel consumption are greater. The greater the delay in communication, the smaller the reduction in emissions and fuel consumption.
published journal article
Phd Dissertation
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.
working paper
This paper analyzes the system organization, performance, and taxi firm impacts of California’s taxi-based elderly and handicapped (E&H) systems, and compares the results to taxi-based general public demand-responsive transit (DRT) systems. The data were gathered from 48 California taxi-based E&H systems. Shared-ride operation is the key to superior system performance, provides the most favorable taxi firm financial impacts and initiates the firm into the paratransit diversification process. In situations with low demand densities, where the sponsor faces a severe total system cost constraint, organizing an ERT system is probably the only feasible strategy. Taxi-based ERT systems are about 25 percent more expensive than taxi-based general public DRT systems, have less impact on firm revenues, and do little to enhance firm evolution.