Nan Jiang

Path-Constrained Traffic Assignment: Model and Algorithm

This paper presents a mathematical programming model and solution method for the path-constrained traffic assignment problem, in which route choices simultaneously follow the Wardropian equilibrium principle and yield the distance constraint imposed on the path. This problem is motivated by the need for modeling distance-restrained electric vehicles in congested networks, but the resulting model and solution method can be applied to various conditions with similar path-based constraints. The equilibrium conditions of the problem reveal that any path cost in the network is the sum of corresponding link costs and a path-specific out-of-range penalty term. The suggested method, based on the classic Frank–Wolfe algorithm, incorporates an efficient constrained shortest-path algorithm as its subroutine. This algorithm fully exploits the underlying network structure of the problem and is relatively easy to implement. Numerical results from the examples of problems provided show how the equilibrium conditions are reshaped by the path constraint and how the traffic flow patterns are affected by different constraint tightness levels.

A network equilibrium analysis on destination, route and parking choices with mixed gasoline and electric vehicular flows

In many countries across the world, fossil fuels, especially petroleum, are the largest energy source for powering the socio-economic system, and the transportation sector dominates the consumption of petroleum in these societies. As the petroleum price continuously climbs and the threat of global climate changes becomes more evident, the world is now facing critical challenges in reducing petroleum consumption and exploiting alternative energy sources. A massive adoption of plug-in electric vehicles (PEVs), especially battery electric vehicles (BEVs), offers a very promising approach to changing the current energy consumption structure and diminishing greenhouse gas emissions and other pollutants. Understanding how individual electric vehicle drivers behave subject to the technological restrictions and infrastructure availability and estimating the resulting aggregate supply–demand effects on urban transportation systems is not only critical to transportation infrastructure development, but also has determinant implications in environmental and energy policy enactment. This paper presents an equilibrium-based analytical tool for quantifying travel choice patterns in urban transportation networks with both gasoline and electric vehicular flows. Specifically, a network equilibrium problem with combined destination, route and parking choices subject to the driving range limit and alternative travel cost composition associated with BEVs are formulated, solved, and numerically analyzed under different network settings and scenarios. The defined problem introduces a new dimension of modeling network equilibrium problems with side constraints. The practical significance of the developed tool lies in its solution tractability and extension capability and its ease of being embedded into the existing urban travel demand forecasting framework.

Network Route Choice Model for Battery Electric Vehicle Drivers with Different Risk Attitudes

Research on the range anxiety of battery electric vehicle (BEV) drivers is limited, and research on the route choice of such drivers has been restricted to a fixed range limit modeled as a distance constrained shortest path problem. In this paper, a more general network route choice model based on the range anxiety of BEV drivers is formulated as a nonadditive shortest path problem. More appropriate for BEVs, a tour-based analysis with a continuum of range limits is considered, and an outer approximation algorithm has been used to find the optimal path. Numerical experiments on a small network demonstrate how the routes taken by BEV drivers are influenced by their risk attitudes and uncertainty in the predicted range of the vehicle.

Economic Analysis of Cargo Cycles for Urban Mail Delivery

Freight constitutes a large portion of urban daily traffic, contributing to emissions, noise, and safety concerns. Moreover, urban freight logistics are often hindered by the last mile, characterized by significant delays and high delivery costs. Firms in dense urban areas are therefore seeking more efficient and reliable modes for their last-mile services. One mode that has been gaining widespread interest is the cargo cycle. Current research on cargo cycles is limited; however, their success in effectively delivering urban goods is gaining recognition. An examination was done of the economic feasibility of using different types of cargo cycles in varying urban contexts. A case study that assessed replacing the U.S. Postal Service vehicles with cargo cycles for last-mile mail deliveries in three population densities is presented. With the use of existing depots, the results of the case study indicate that electric cargo trikes have the lowest net present value among the modes in congested areas with high population densities such as central business districts. Moreover, having a depot positioned within the delivery area was found to play a significant role in increasing the competitiveness of trikes compared with other modes.

Finding and Exploring Use of Commodity-Specific Data Sources for Commodity Flow Modeling

Commodity flow modeling studies rely on traditional data sources, such as the Commodity Flow Survey, the Freight Analysis Framework, Transearch, surveys, the U.S. census, county business patterns, and input–output models. The strengths and shortcomings of those data sources have been evaluated in the literature; the sources can be useful for modeling, but they do not necessarily support a supply chain approach or provide the level of detail or accuracy desired for modeling a particular commodity’s supply chain and flow on a city or state roadway network. This paper expands on the work of NCFRP Report 35: Implementing the Freight Transportation Data Architecture: Data Element Dictionary by finding existing data sources unique to specific commodities that identify key supply chain locations and industry relationships and that provide more detail about commodity quantity and movement to overcome the limitations of traditional freight data sources. The goal of the investigation was to find more data sets to use in commodity flow modeling. For each commodity, this paper describes data sources found, data attributes, and how those data were used to estimate flow from origins and destinations within supply chain links. The commodity-specific approach opens doors to sources of data not normally incorporated into transportation research.

Computation of Skims for Large-Scale Implementations of Integrated Activity-Based and Dynamic Traffic Assignment Models

Integrated activity-based model (ABM) and dynamic traffic assignment (DTA) frameworks have emerged as promising tools to support transportation planning and operations, particularly in the context of novel technologies and data sources. This research proposes an approach to characterize the implementation of integrated ABM-DTA models and seeks to facilitate the interpretation and comparison of frameworks and, ultimately, the selection of appropriate tools. The importance of the dimensions considered in this characterization is illustrated through a detailed analysis of the computation of skims. Skims are the level of service (LOS) metric produced by DTA models, and the computation of skims may impact the performance and convergence of ABM-DTA applications. Numerical results from experiments on a regional ABM-DTA model in Austin, Texas, suggest that skims produced at relatively small time steps (10 to 30 min) may lead to a faster integrated model convergence. Finer time-grained skims are also observed to capture sharper temporal peaking patterns in the LOS. This work considers two skim computation methods; the analysis of the results suggests that simpler techniques are adequate, as the inherent variability of travel times from simulation overshadows any gain in precision from more complex methods. This study also uses promising techniques to visualize and analyze the model results, a challenging task in the context of highly disaggregate models and the subject of further research. The insights from this research effort can inform future research on the implementation of ABM-DTA methods and practical applications of existing frameworks.