Effect of providing traffic information estimated by a stochastic network equilibrium model with stochastic demand

Abstract

To estimate travel times through road networks, in this study, we assume a stochastic demand and formulate a stochastic network equilibrium model whose travel times, flows, and demands are stochastic. This model enables us to examine network reliability under stochastic circumstances and to evaluate the effect of providing traffic information on travel times. For traffic information, we focus on travel time information and propose methods to evaluate the effect of providing that information. To examine the feasibility and validity of the proposed model and methods, we apply them to a simple network and the real road network of Kanazawa, Japan. The results indicate that providing ambulance drivers in Kanazawa with travel time information leads to an average reduction in travel time of approximately three minutes. Keywords:stochastic network equilibrium, emergency vehicles, travel time information provision stochastic route choice, and developed a second-order stochastic network equilibrium with stochastic flows.A main cause of network uncertainty is variation in travel demand; thus, stochastic demand should be incorporated into network equilibrium models. Shao et al. (2006) and Siu & Lo (2008) introduced stochastic demands into network equilibrium models and explicitly considered stochastic flows. Lam et al. (2008) and Sumalee et al. (2010) considered network flow under stochastic supply and demand, and examined the impacts of adverse weather conditions. Zhang et al. (2011), who similarly considered stochastic supply and demand, introduced the expected residual minimization into stochastic-flow network equilibrium. Nakayama & Watling (2014) proposed stochastic network equilibrium models with stochastic flows, in which stochastic travel time, network flow, and demand are treated consistently. However, they assumed that the stochastic travel demands are independent among origin-destination (OD) pairs.In this study, we improve upon the model of Nakayama & Watling (2014), alleviating the independent demand and other assumptions, and formulate a stochastic network equilibrium model under stochastic demands. Using the proposed stochastic network equilibrium model, we develop methods to evaluate the effect of providing travel time information. Then, we apply the model and methods to a simple network and the real road network of Kanazawa, Japan, and examine their applicability and validity. Assumptions for Drivers and NotationThe notation used in this study is as follows: I = the number of OD pairs A = the total number of links J i = the number of routes between OD pair i J = the total number of all routes (= J 1 + J 2 +⋅⋅⋅+ J I ) K = the total number of latent drivers δ a,ij = link route incidence variable (δ a,ij = 1 if link a is part of route j between OD pair i; 0 otherwise) σ σ σ J J J J J J J J J J

Statistical inference of probabilistic origin-destination demand using day-to-day traffic data

“…where the random variable Q are generated from a standard continuous MVN Q and then rounded to the nearest integer. When the number of travelers is sufficiently large, the rounding error is negligible [51]. In this paper demand is approximated by a continuous MVN.…”

“…All these studies indicate the O-D variation is of great importance to network modeling and behavioral analysis. Statistical traffic assignment models consider various O-D probability distributions such as Poisson distributions [19], MVN [13], multinomial distributions [51]. Nakayama and Watling [54] summarized different formulations and proposed a unified framework for stochastic modeling of traffic flows.…”

Measuring and reducing the disequilibrium levels of dynamic networks with ride-sourcing vehicle data

“…As DUE implies network traffic conditions are precisely known to all travelers and they all make the efforts reaching the optimal routes, many studies challenged the DUE in modeling travelers' behaviors and network conditions in real-world networks [37]. Levinson [26], Ben-Elia et al [3], Nakayama [36] showed the accuracy of traffic information provided to travelers has a significant impact on their route selection and the resulting network conditions. Zhu and Levinson [64], Jan et al [22] conducted empirical studies using GPS data and found that most travelers do not follow shortest paths in the network, and some studies indicate that travelers are likely to follow the shortest distance paths [1,44] or hyperpaths [30].…”

A two-stage policy for improving the efficiency of the traffic network by providing value-added ATIS service

“…However, due to the severe competition in the ATIS market, nowadays almost all ATIS service providers (e.g., Goggle Map or Amap) offer the basic route guidance service for free. In this case, they begin to seek for profit by providing the value-added ATIS (VATIS) service based on the evidence that people are willing to pay to the save travel time [1][2][3][4][5]. As an actual example, Amap offers several kinds of valueadded services to travelers, such as taxi service, hotel reservation, and online coupons obtaining, which are bringing Amap a tidy profit.…”