Abstract
Adverse weather condition is one of the inducements that lead to supply uncertainty of an urban transportation system, while travelers’ multiple route choice criteria are the nonignorable reason resulting in demand uncertainty. This paper proposes a novel stochastic traffic network equilibrium model considering impacts of adverse weather conditions on roadway capacity and route choice criteria of two-class mixed roadway travellers on demand modes, in which the two-class route choice criteria root in travelers’ different network information levels (NILs). The actual route travel time (ARTT) and perceived route travel time (PRTT) are considered as the route choice criteria of travelers with perfect information (TPI) and travelers with bounded information (TBI) under adverse weather conditions, respectively. We then formulate the user equilibrium (UE) traffic assignment model in a variational inequality problem and propose a solution algorithm. Numerical examples including a small triangle network and the Sioux Falls network are presented to testify the validity of the model and to clarify the inner mechanism of the two-class UE model under adverse weather conditions. Managerial implications and applications are also proposed based on our findings to improve the operation efficiency of urban roadway network under adverse weather conditions.
Highlights
Uncertainties of transportation networks are logically derived from supply side and demand side [1,2,3,4], which can be considered as exogenous and endogenous sources [5], respectively
On account of the overwhelming majority of transportation modes being directly exposed in the atmospheric environment, the total process of traffic activity is inevitably more or less subject to adverse weather conditions, such as rainfall, snow, fog, and high winds, etc. [6]. erefore, adverse weather conditions are widely recognized as a primary inducement markedly generating the uncertainties of roadway capacity
According to the route travel time in the equilibrium state of this example (formula (26), Figures 6(a) and 6(b)), no matter which class a traveler belongs to, the route travel time in developed area (O-D pair 2–4) (i.e., 0.81 and 0.82 for travelers with perfect information (TPI) and travelers with bounded information (TBI), respectively) is higher than that in developing area (O-D pair 1–4), which results from the following reasons: (1) the traffic demand (2000) in developed area (O-D pair 2–4) is higher than that (1500) in developing area (O-D pair 1–4); (2) high ATIS penetration rate in developed area intensifies the travelers’ aggregation effects on “weather-resistant” routes to seek for lower travel time cost under adverse weather conditions
Summary
Uncertainties of transportation networks are logically derived from supply side (roadway capacity fluctuation) and demand side (travel demand variation) [1,2,3,4], which can be considered as exogenous and endogenous sources [5], respectively. As a vital inducement to the travel time uncertainty, adverse weather condition has been understanding and modeling its relationship with travelers’ route choice decisions [12]. Erefore, in this study, to model the network equilibrium problem more realistically, we intuitively assume that the traveler population are divided into two exclusive classes by their different network information levels (NILs) under adverse weather conditions: (1) with the equipment with ATIS, one part of travelers who can acquire a perfect knowledge of actual, scenario-specific network condition (i.e., travelers with perfect information, TPI) would fully choose the recommended actual shortest route with minimum actual travel cost, which accords with the wisdom of conventional user equilibrium [16], (2) analogous to the traveler feature that depicted in Lam et al [13], the other part of travelers, who are clustered without the actual information of the network and named as travelers with bounded information (TBI), choose their routes with minimum perceived travel cost according to their individual travelling experiences and perceptions on the information of weather forecast.
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