James Vaughan

Bio: James Vaughan is an academic researcher from University of Toronto. The author has contributed to research in topics: Greenhouse gas & Transport engineering. The author has an hindex of 3, co-authored 7 publications receiving 54 citations.

Calibration of large-scale transport planning models: a structured approach

Abstract: Traditionally, transport planning model systems are estimated and calibrated in an unstructured way, which does not allow for interactions among included parameters to be considered. Furthermore, the computational burden of model systems plays a key role in choosing a calibration approach, and usually forces modellers to calibrate demand-side and network models separately. Also, trial-and-error methods and expert opinion are currently the backbones of transport model calibration, which leaves room for error in the calibrated parameters. This paper addresses these challenges and suggests a structured approach for determining optimal calibrated transport model parameters. This approach involves joint estimation and calibration of demand and network models, with a major focus on avoiding any manipulation of the OD matrix. The approach can be applied to static or dynamic traffic assignments. The approach is applied by calibrating GTAModel—an example of a large-scale agent-based model system from Toronto, Canada.

Calculating place-based transit accessibility: Methods, tools and algorithmic dependence

Abstract: To capture the complex relationships between transportation and land use, researchers and practitioners are increasingly using place-based measures of transportation accessibility to support a broad range of planning goals. This research reviews the state-of-the-art in applied transportation accessibility measurement and performs a comparative evaluation of software tools for calculating accessibility by walking and public transit including ArcGIS Pro, Emme, R5R, and OpenTripPlanner using R and Python, among others. Using a case study of Toronto, we specify both origin-based and regional-scale analysis scenarios and find significant differences in computation time and calculated accessibilities. While the calculated travel time matrices are highly correlated across tools, each tool produces different results for the same origin-destination pair. Comparisons of the estimated accessibilities also reveal evidence of spatial clustering in the ways paths are calculated by some tools relative to others at different locations around the city. With the growing emphasis on accessibility-based planning, analysts should approach the calculation of accessibility with care and recognize the potential for algorithmic dependence in their calculated accessibility results.

Modelling cellphone trace travel mode with neural networks using transit smartcard and home interview survey data

Abstract: This study proposes a framework to impute travel mode for trips identified from cellphone traces by developing a deep neural network model. In our framework, we use the trips from a home interview survey and transit smartcard data, for which the travel mode is known, to create a set of artificial pseudo-cellphone traces. The generated artificial pseudo-cellphone traces with known mode are then used to train a deep neural network classifier. We further apply the trained model to infer travel modes for the cellphone traces from cellular network data. The empirical case study region is Montevideo, Uruguay, where high-quality data are available for all three types of data used in the analysis: a large dataset of cellphone traces, a large dataset of public transit smartcard transactions, and a small household travel survey. The results can be used to create an enhanced representation of origin-destination trip-making in the region by time of day and travel mode.

Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions

Abstract: In this paper, we study an electric vehicle routing problem while considering the constraints on battery life and battery swapping stations. We first introduce a comprehensive model consisting of speed, load and distance to measure the energy consumption and carbon emissions of electric vehicles. Second, we propose a mixed integer programming model to minimize the total costs related to electric vehicle energy consumption and travel time. To solve this model efficiently, we develop an adaptive genetic algorithm based on hill climbing optimization and neighborhood search. The crossover and mutation probabilities are designed to adaptively adjust with the change of population fitness. The hill climbing search is used to enhance the local search ability of the algorithm. In order to satisfy the constraints of battery life and battery swapping stations, the neighborhood search strategy is applied to obtain the final optimal feasible solution. Finally, we conduct numerical experiments to test the performance of the algorithm. Computational results illustrate that a routing arrangement that accounts for power consumption and travel time can reduce carbon emissions and total logistics delivery costs. Moreover, we demonstrate the effect of adaptive crossover and mutation probabilities on the optimal solution.

Simulating impacts of automated driving behavior and traffic conditions on vehicle emissions

Abstract: Automated vehicles (AV) will have an impact on the movement of people and goods. Assessing the effects of AVs on land use, congestion and the environment is of utmost importance in order to inform policy and planning decisions. This study explores the effects of AVs and vehicle electrification on greenhouse gas emissions using traffic microsimulation and emission modeling. The driving behavior modeling parameters most relevant to AVs are tested within ranges representing potential AV operations. The effects of AVs are evaluated under both uninterrupted and interrupted traffic flow conditions, representing highway driving and driving on arterial roads, under high and low traffic demand. The main findings indicate that AVs can bring positive changes in terms of emissions and traffic flow performance. The effects are more evident when AVs are tuned to more aggressive driving settings (i.e., drive closer together) and especially under high levels of traffic demand in an uninterrupted flow setting (highway). When AVs are programmed to operate more aggressively, the subsequent emission factors could be reduced by up to 26% on the expressway, while cautiously programmed AVs could deteriorate traffic performance and lead to a 35% increase in emissions.