Prateek Bansal

Operations of Shared Autonomous Vehicle Fleet for Austin, Texas, Market

The emergence of automated vehicles holds great promise for the future of transportation. Although commercial sales of fully self-driving vehicles will not commence for several more years, once these sales are possible a new transportation mode for personal travel promises to arrive. This new mode is the shared autonomous (or fully automated) vehicle (SAV), combining features of short-term, on-demand rentals with self-driving capabilities: in essence, a driverless taxi. This investigation examined the potential implications of the SAV at a low level of market penetration (1.3% of regional trips) by simulating a feet of SAVs serving travelers in the 12-mi by 24-mi regional core of Austin, Texas. The simulation used a sample of trips from the regions planning model to generate demand across traffic analysis zones and a 32,272-link network. Trips called on the vehicles in 5-min departure time windows, with link-level travel times varying by hour of day based on MATSIMs dynamic traffic assignment simulation software. Results showed that each SAV could replace about nine conventional vehicles within the 24-mi by 12-mi area while still maintaining a reasonable level of service (as proxied by user wait times, which averaged just 1 min). Additionally, approximately 8% more vehicle miles traveled (VMT) may be generated because of SAVs ability to journey unoccupied to the next traveler or relocate to a more favorable position in anticipation of its next period demand.

HYBRID ELECTRIC VEHICLE OWNERSHIP AND FUEL ECONOMY ACROSS TEXAS: AN APPLICATION OF SPATIAL MODELS

Policymakers, transport planners, automobile manufacturers, and others are interested in the factors that affect adoption rates of electric vehicles and other fuel-efficient vehicles. With tract-level data from the 2010 census and registered vehicle counts from Texas counties in 2010, this study investigated the impact of various built environment and demographic attributes, including land use balance, employment density, population density, median age, gender, race, education, household size, and income. Spatial autocorrelation (across census tracts) in unobserved components of vehicle counts by tract and cross-response correlation (both spatial and local–aspatial in nature) was allowed for by the estimation of models of ownership levels (vehicle counts by vehicle type and fuel economy level) with bivariate and trivariate Poisson–lognormal conditional autoregressive models. The presence of high spatial autocorrelations and local cross-response correlations was consistent in all models across all counties studied. Ownership rates for fuel-efficient vehicles were found to rise with household income, resident education levels, and the share of male residents and to fall in the presence of larger household sizes and higher job densities. The average fuel economy of each tracts light-duty vehicles was also analyzed with a spatial error model across all Texas tracts, and this variable was found to depend most on educational attainment levels, median age, income, and household size variables, though all covariates used were statistically significant. If households registering more fuel-efficient vehicles, including hybrid electric vehicles, are also more inclined to purchase plug-in electric vehicles, these findings can assist in spatial planning of charging infrastructure as well as other calculations (such as implications for the revenue from gas tax).

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

Abstract 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.

Impacts of Bus-stops on the Speed of Motorized Vehicles under Heterogeneous Traffic Conditions: A Case-Study of Delhi, India

The presence of friction generators1 such as bus-stops, intersections, petrol pumps and pedestrian crossings, etc. significantly influences the speed of traffic stream. Among all the friction generators, understanding the impact of bus-stops is particularly important from planning and modeling perspective in the Indian context. Therefore, this study presents a methodology to quantify the impact of bus-stops on the speed of other motorized vehicles (the total motorized vehicle fleet minus the buses) under heterogeneous traffic conditions. The methodology was validated on the typical urban arterials in Delhi, India. Two types of data, location of bus-stops and speed profiles of motorized vehicles, were collected by GPS and V-box respectively. These two data sets were mapped and merged using ArcGIS. To understand the nature of traffic stream near bus-stops, ‘influence regions’ of bus-stops were extracted. Later, characteristic parameters such as lengths of the influence regions and average speeds in the infl...