Stochastic Route Choice Model Research Articles

Comparing Dynamic User Equilibrium and Noniterative Stochastic Route Choice in a Simulation-Based Dynamic Traffic Assignment Model: Practical Considerations for Large-Scale Networks

Simulation-based dynamic traffic assignment (DTA) models play a vital role in transportation planning and operations. While the widely studied equilibrium-seeking DTA including dynamic user equilibrium (DUE) often provides robust and consistent outcomes, their expensive computational cost for large-scale network applications has been a burden in practice. The noniterative stochastic route choice (SRC) model, as a nonequilibrium seeking DTA model, provides an alternative for specific transportation operations applications that may not require equilibrium results after all (e.g., evacuation and major network disruptions) and thus tend to be computationally less expensive, yet may suffer from inconsistent outcomes. While DUE is a widely accepted approach for many strategic planning applications, SRC has been increasingly used in practice for traffic operations purposes. This paper aims to provide a comparative and quantitative analysis of the two modeling approaches. Specifically, a comparison has been made at two levels: link-level flows and network-level congestion patterns. Results suggest that adaptive driving improves the quality of the SRC solution, but its difference from DUE still remains significant at the link level. Results have practical implications for the application of large-scale simulation-based DTA models for planning and operations purposes.

A hybrid gravity and route choice model to assess vector traffic in large-scale road networks.

Human traffic along roads can be a major vector for infectious diseases and invasive species. Though most road traffic is local, a small number of long-distance trips can suffice to move an invasion or disease front forward. Therefore, understanding how many agents travel over long distances and which routes they choose is key to successful management of diseases and invasions. Stochastic gravity models have been used to estimate the distribution of trips between origins and destinations of agents. However, in large-scale systems, it is hard to collect the data required to fit these models, as the number of long-distance travellers is small, and origins and destinations can have multiple access points. Therefore, gravity models often provide only relative measures of the agent flow. Furthermore, gravity models yield no insights into which roads agents use. We resolve these issues by combining a stochastic gravity model with a stochastic route choice model. Our hybrid model can be fitted to survey data collected at roads that are used by many long-distance travellers. This decreases the sampling effort, allows us to obtain absolute predictions of both vector pressure and pathways, and permits rigorous model validation. After introducing our approach in general terms, we demonstrate its benefits by applying it to the potential invasion of zebra and quagga mussels (Dreissena spp.) to the Canadian province British Columbia. The model yields an R2-value of 0.73 for variance-corrected agent counts at survey locations.

Calibration of C-Logit-Based SUE Route Choice Model Using Mobile Phone Data

Theoretically speaking, the data of a stated preference survey could be suggested for the calibration of a stochastic route choice model. However, it is unrealistic to implement the questionnaire survey for such a large number of alternative routes. Engineers generally determine the parameter empirically. This experienced choice of perception parameter may cause higher errors in the route flows. In our calibration model of the perception parameter, the data of the cellular network is set as the input. This model consists of two levels. The upper level is to minimize the gap squares of the route choice ratio between the C-logit model and the cellular network data. The stochastic user equilibrium (SUE) in terms of the C-logit model is used as the lower level. The simulated annealing (SA) algorithm is used to solve the model, where the route-based gradient projection (GP) algorithm is used to solve the inner SUE. A case study is used to validate the convergence of the model calibration. A real-world road network is used to demonstrate the objective advantage of an equilibrium constraint over a nonequilibrium constraint and explain the feasibility of the candidate routes assumption.

Stochastic route choice models based on VMS information and hierarchy for urban transport network

This paper firstly establishes four variational inequalities for stochastic traffic assignment by quantifying information utility of Variable Message Signs (VMS) and hierarchy of transport network. When VMS systems are operated in the higher level of roads such as arterials, the attenuation factor of information utility is integrated with information quantity to define a new formulation of the impact coefficient of VMS on traffic flow, which appears in the definitions of link and path lengths, and allocation parameters. Furthermore, equivalent Variational Inequalities (VIs) of Logit models considering these elements are developed and proved. A case study was conducted using the VI models on a hierarchical road network. We get some practically meaningful results, for example, VMS information can improve hierarchical network performance by interacting with traffic flow in all models except the C-Logit model and hierarchical level can improve network performance. Among the results, the most important is that the C-Logit or path-size Logit will produce a flow pattern with less fluctuation of traffic densities among links and is less affected by stochastic factors than the multinomial Logit and cross-nested Logit in a stochastic hierarchical road network.

A Maximum Entropy Fixed-Point Route Choice Model for Route Correlation

In this paper we present a stochastic route choice model for transit networks that explicitly addresses route correlation due to overlapping alternatives. The model is based on a multi-objective mathematical programming problem, the optimality conditions of which generate an extension to the Multinomial Logit models. The proposed model considers a fixed point problem for treating correlations between routes, which can be solved iteratively. We estimated the new model on the Santiago (Chile) Metro network and compared the results with other route choice models that can be found in the literature. The new model has better explanatory and predictive power that many other alternative models, correctly capturing the correlation factor. Our methodology can be extended to private transport networks.

A Fixed Point Route Choice Model for Transit Networks that Addresses Route Correlation

In this paper we present a stochastic route choice model for transit networks, which explicitly addresses route correlation due to overlapping in the alternatives. The model is based on a multi-objective mathematical programming problem, which optimality conditions generate an extension to the Multinomial Logit models. The model proposed considers a fixed point problem for treating correlation between routes, which can be solved iteratively. We estimated the new model on the Santiago Metro network and compared the results with other route choice models that can be found in literature. The new model has better explanatory and predictive power, correctly capturing the correlation factor. Our methodology can be extended to private transport networks.

A Simulation‐Based Dynamic Stochastic Route Choice Model for Evacuation

This paper establishes a dynamic stochastic route choice model for evacuation to simulate the propagation process of traffic flow and estimate the stochastic route choice under evacuation situations. The model contains a lane‐group‐based cell transmission model (CTM) which sets different traffic capacities for links with different turning movements to flow out in an evacuation situation, an actual impedance model which is to obtain the impedance of each route in time units at each time interval and a stochastic route choice model according to the probit‐based stochastic user equilibrium. In this model, vehicles loading at each origin at each time interval are assumed to choose an evacuation route under determinate road network, signal design, and OD demand. As a case study, the proposed model is validated on the network nearby Nanjing Olympic Center after the opening ceremony of the 10th National Games of the People′s Republic of China. The traffic volumes and clearing time at five exit points of the evacuation zone are calculated by the model to compare with survey data. The results show that this model can appropriately simulate the dynamic route choice and evolution process of the traffic flow on the network in an evacuation situation.

A stochastic route choice model for car travellers in the Copenhagen region

The paper presents a large-scale stochastic road traffic assignment model for the Copenhagen Region. The model considers several classes of passenger cars (different trip purposes), vans and trucks, each with its own utility function on which route choices are based. The utility functions include distributed coefficients (Error Components) estimated on SP-data in a mixed logit model. This was compared with a traditional nested logit model. A number of alternative formulations of EC were tested, and the resulting distributions of value of times are discussed. In application, the different classes and types of vehicles influence all the speed-flow relationships on links within an equilibrium framework. Sub-models for intersections and roundabouts describe queues and geometric delays.

Comparisons of Deterministic and Stochastic Traffic Loading Models

Stochastic route choice models have been proposed as a more realistic alternative to the simpler deterministic route choice representations in models for loading traffic onto a network. However, studies on stochastic route choice models have thus far been limited to small experimental networks. Because stochastic models are in general considered to be more realistic in their assumptions than deterministic models, it becomes pertinent to examine their implementation and the corresponding traffic loading on a network of realistic size. Such an implementation for two stochastic route choice models is examined. The results indicate that the differences among the models, at the network level, for predicted link flows and average travel times between origin and destinations are not major, and therefore, deterministic models may suffice for long-range network planning. However, stochastic models predict the usage of more links in the network, and the difference in the flows at the link level may play an important role in travel advisory and route guidance systems. Some suggestions for future research are presented.