Integration of Environmental Objectives in a System Optimal Dynamic Traffic Assignment Model

Abstract

Maintaining air‐quality standards is a major concern for transportation planners and policy makers in the United States. This necessitates considering nontraditional emission objectives in transportation systems modeling. In this research, we integrate emission‐based objective into the traditional travel time based dynamic assignment framework. Carbon monoxide (CO) emissions from vehicles are computed as functions of space mean speed (determined from an embedded mesoscopic traffic flow model). Different performance metrics (CO emission, system wide travel time, and speed profiles) from the integrated model are compared with traditional dynamic assignment model (with travel time minimization objective). In addition, results indicate changes in route choice behavior of the road users when emission objective is integrated to dynamic assignment framework.

Link-based system optimum dynamic traffic assignment problems with environmental objectives

“…There are three major types of objectives in existing SO-DTA models: minimizing total system travel time (TSTT) (e.minimizing both TSTT and TSE for a whole network in an integrated manner (e.g., Aziz and Ukkusuri, 2012;Ma et al, 2015). Most existing SO-DTA models accommodate only network mobility, and are used to meet the first of the above objectives: to minimize the TSTT spent by travelers in a network.…”

“…Based on the travel times estimated from the decomposed link inflows, we develop two methods of evaluating link emissions. In contrast with existing methods (e.g., Aziz and Ukkusuri, 2012;Ma et al, 2015), the proposed methods of estimating link emissions can be integrated with any average speed based emission functions. In addition, we use Yperman's (2007) link transmission model (LTM) to represent dynamic network constraints and NVH constraints on SO-DTA problems in single-destination networks.…”

“…Part D (2016), http://dx.doi.org/10.1016/j.trd.2016.06.003 (e.g., Lin and Wang, 2004;Pavlis and Recker, 2009), and can thus be more easily solved than continuous-time SO-DTA models. However, this category of models also compromise computational tractability in large-scale network applications due to the presence of numerous decision variables and constraints.There are three major types of objectives in existing SO-DTA models: minimizing total system travel time (TSTT) (e.minimizing both TSTT and TSE for a whole network in an integrated manner (e.g., Aziz and Ukkusuri, 2012;Ma et al, 2015). Most existing SO-DTA models accommodate only network mobility, and are used to meet the first of the above objectives: to minimize the TSTT spent by travelers in a network.…”

“…However, the accuracy of the approximation is only high when the network is very congested, and the model is limited to a particular carbon monoxide (CO) emission function. To address the third objective, SO-DTA models are developed with weighted TSTT and TSE components and capture the trade-off between emissions and travel time (e.g., Aziz and Ukkusuri, 2012;Ma et al, 2015).Dynamic network constraints are generally used to formulate feasible domains for existing SO-DTA models. There are four categories of dynamic network constraints: mass balance constraints, flow conservation constraints, flow propagation constraints, and definitional constraints.…”

Urban Freight Truck Routing under Stochastic Congestion and Emission Considerations

“…We incorporated into the total travel cost not only the stochastic truck travel time, but also cost induced by various emissions and a penalty for late or early truck arrival. Although significant efforts have been made recently in combining environmental effects from vehicle emissions with various transportation studies, they focus on either deterministic network optimization problems [37][38][39][40] or traffic control problems such as signal timing optimization at urban intersections [41,42]. In this study, various vehicle emissions are assumed to follow U-shaped functions with respect to vehicle speed [43].…”