Routing Policy Choice Set Generation in Stochastic Time-Dependent Networks

Adaptive route choice models are studied that explicitly capture travelers’ route choice adjustments according to information on realized network conditions in stochastic time-dependent networks. Two types of adaptive route choice models are explored: an adaptive path model in which a sequence of path choice models are applied at intermediate decision nodes and a routing policy choice model in which the alternatives correspond to routing policies rather than paths at the origin. A routing policy in this study is a decision rule that maps from all possible pairs (e.g., node, time) to the next links out of the node. Existing route choice models that can be estimated on disaggregate revealed preferences assume a deterministic network setting from the traveler's perspective and cannot capture the traveler's proactive adaptive behavior under uncertain traffic conditions. The literature includes a number of algorithmic studies of optimal routing policy problems, but the estimation of a routing policy choice model is a new research area. The specifications of estimating the two adaptive route choice models are established and the feasibility of estimation from path observations is demonstrated on an illustrative network. Prediction results from three models–nonadaptive path model, adaptive path model, and routing policy model–are compared. The routing policy model is shown to better capture the option value of diversion than the adaptive path model. The difference between the two adaptive models and the nonadaptive model is larger in terms of expected travel time if the network is more stochastic, indicating that the benefit of adaptivity is more significant in a more unpredictable network.

A variational autoencoder approach for choice set generation and implicit perception of alternatives in choice modeling

This article addresses the generation of choice sets for the purposes of route choice analysis and flow prediction in uni-modal and multi-modal networks. Ample attention is devoted to the implications for choice set generation of these various purposes. Based on a theory on choice behavior, a new model-based choice set generation approach will be elaborated, called the doubly stochastic choice set generation model, meant for establishing choice sets prior to the choice modeling step. Because of its stochastic principle, a typical property of the proposed generation approach is that the size and composition of the generated choice sets are stochastic variables. We will devote ample attention to these stochastic properties using theoretical derivations and experimental studies. The article reports on the calibration of this generation approach for multi-modal networks and illustrates the approach with a number of predictions in various networks.

Two of the most prominent factors influencing driver’s route choice behaviour in an urban or suburban environment are the large number of alternatives available to the decision-maker and the similarity between route alternatives, both caused by the high density of urban and suburban street networks. Regarding the modelling of route choice behaviour this high density influences the way the choice set is established as well as the way the overlap between route alternatives is accounted for. This paper addresses both issues for car route choice observations derived from GPS data. Different choice set generation procedures as well as choice set sizes are evaluated regarding their effect on the choice set composition and the resulting route choice models. In addition, the impact of different route attributes is investigated. The focus, however, will be put on the analysis of the similarity factors, which account for route overlap. Different formulations will be tested in order to evaluate which mechanisms are at work in car route choice in an urban or suburban context.

Route choice is one of the most complex decision-making contexts to represent mathematically, and the most frequently used approach to model route choice consists of generating alternative routes and modeling the preferences of utility-maximizing travelers. The main drawback of this approach is the dependency of the parameter estimates from the choice set generation technique. Bias introduced in model estimation has been corrected only for the random walk algorithm, which has problematic applicability to large-scale networks. This study proposes a correction term for the sampling probability of routes extracted with stochastic route generation. The term is easily applicable to large-scale networks and various environments, given its dependence only on a random number generator and the Dijkstra shortest path algorithm. The implementation for revealed preferences data, which consist of actual route choices collected in Cagliari, Italy, shows the feasibility of generating routes stochastically in a high-resolution network and calculating the correction factor. The model estimation with and without correction illustrates how the correction not only improves the goodness of fit but also turns illogical signs for parameter estimates to logical signs.

This study introduces the novel concept of local detouredness, i.e. detours on subsections of a route, as a new phenomenon for understanding and modelling route choice. Traditionally, Stochastic User Equilibrium (SUE) traffic assignment models have been concerned with judging the attractiveness of a route by its total route cost. However, through empirical analysis we show that considering solely the global properties of a route is insufficient. We find that it is important to consider local detouredness both when determining realistic and tractable route choice sets and when determining route choice probabilities. For example, analysis of observed route choice data shows that route usage tends to decay with local detouredness, and that there is an apparent limit on the amount of local detouredness seen as acceptable. No existing models can account for this systematically and consistently, which is the motivation for the new route choice model proposed in this paper: the Bounded Choice Model with Local Detour Threshold (BCM-LDT). The BCM-LDT model incorporates the effect of local detouredness on route choice probability, and has an in-built mechanism that assigns zero probabilities to routes violating a bound on total route costs and/or a threshold on local detouredness. Thereby, the model consistently predicts which routes are used and unused. Moreover, the probability expression is closed-form and continuous. SUE conditions for the BCM-LDT are given, and solution existence is proven. Exploiting the special structure of the problem, a novel solution algorithm is proposed where flow averaging is integrated with a modified branch-and-bound method that iteratively column-generates all routes satisfying local and global bounds. Numerical experiments are conducted on small-scale and large-scale networks, establishing that equilibrated solutions can be found and demonstrating the influence of the BCM-LDT parameters on choice set size and flow allocation.

This paper discusses choice set generation and route choice model estimation for large-scale urban networks. Evaluating the effectiveness of Advanced Traveler Information Systems (ATIS) requires accurate models of how drivers choose routes based on their awareness of the roadway network and their perceptions of travel time. Many of the route choice models presented in the literature pay little attention to empirical estimation and validation procedures. In this paper, a route choice data set collected in Boston is described and the ability of several different route generation algorithms to produce paths similar to those observed in the survey is analyzed. The paper also presents estimation results of some route choice models recently developed using the data set collected.

This paper considers the problem of throughput optimal routing/scheduling in\na multi-hop constrained queueing network with random connectivity whose special\ncase includes opportunistic multi-hop wireless networks and input-queued switch\nfabrics. The main challenge in the design of throughput optimal routing\npolicies is closely related to identifying appropriate and universal Lyapunov\nfunctions with negative expected drift. The few well-known throughput optimal\npolicies in the literature are constructed using simple quadratic or\nexponential Lyapunov functions of the queue backlogs and as such they seek to\nbalance the queue backlogs across network independent of the topology. By\nconsidering a class of continuous, differentiable, and piece-wise quadratic\nLyapunov functions, this paper provides a large class of throughput optimal\nrouting policies. The proposed class of Lyapunov functions allow for the\nrouting policy to control the traffic along short paths for a large portion of\nstate-space while ensuring a negative expected drift. This structure enables\nthe design of a large class of routing policies. In particular, and in addition\nto recovering the throughput optimality of the well known backpressure routing\npolicy, an opportunistic routing policy with congestion diversity is proved to\nbe throughput optimal.\n

We present a new routing policy, called maximum shortest paths (MP) routing policy, within the class of shortest-path routing policies for mesh-connected topologies which include popular 2-D and 3-D meshes, 2-D and 3-D tori, and n-dimensional hypercubes (n-cubes). In this policy, the routing message is always forwarded to a neighbor from which there exists a maximum number of shortest paths to the destination. An optimal routing defined in this paper is the one that maximizes the probability of reaching the destination from a given source without delays at intermediate nodes. We show that the MP routing policy is equivalent to the e-cube routing in n-cubes which is optimal, and it is also equivalent to the Badr and Podar's zig-zag (Z/sup 2/) routing policy in 2-D meshes which is also optimal. We prove that the Z/sup 2/ routing policy is not optimal in any N/spl times/N torus, where N is an even number larger than four. A routing algorithm is proposed to implement the MP routing policy in 2-D tori and if is proved to be at least suboptimal (optimal for some cases). Our approach is the first attempt to address optimal routing in the torus network which is still an open problem.

A Logit-based route choice model is proposed to address the overlapping and scaling problems in the traditional multinomial Logit model. The nonoverlapping links are defined as a subnetwork, and its equivalent impedance is explicitly calculated in order to simply network analyzing. The overlapping links are repeatedly merged into subnetworks with Logit-based equivalent travel costs. The choice set at each intersection comprises only the virtual equivalent route without overlapping. In order to capture heterogeneity in perception errors of different sizes of networks, different scale parameters are assigned to subnetworks and they are linked to the topological relationships to avoid estimation burden. The proposed model provides an alternative method to model the stochastic route choice behaviors without the overlapping and scaling problems, and it still maintains the simple and closed-form expression from the MNL model. A link-based loading algorithm based on Dial’s algorithm is proposed to obviate route enumeration and it is suitable to be applied on large-scale networks. Finally a comparison between the proposed model and other route choice models is given by numerical examples.

With the increasing use of GPS in transport surveys, analysts can not only choose from numerous new ways to model travel behaviour but also face several new challenges. For instance, information about chosen routes is now available with a high level of spatial and temporal accuracy. However, advanced post-processing is necessary to make this information usable for route choice modelling. Out of many related issues, this article focuses on the generation of choice sets for car trips extracted from GPS data. The aim is to generate choice sets for about 36,000 car trips made by 2434 persons living in and around Zurich, Switzerland, on the Swiss Navteq network, a very high-resolution network. This network resolution is essential for an accurate identification of chosen routes. However, it substantially increases the requirements for the choice set generation algorithm in regard to performance as well as choice set composition. This article presents a new route set generation based on shortest path search with link elimination. The proposed procedure combines a Breadth First Search with a topologically equivalent network reduction and ensures a high diversity between the routes, as well as computational feasibility for large-scale problems like the one described above. To demonstrate the usability of the algorithm, its performance and the resulting route sets are compared with those of a stochastic choice set generation algorithm.

Route choice of bike share users: Leveraging GPS data to derive choice sets

Switching threshold of commuting Travelers’ route choice under different patterns of information

A random utility based estimation framework for the household activity pattern problem

In this paper, online stochastic routing policies are developed to identify the optimal next action (path choice) at the current decision node (intersection) for travelers on the basis of their preference for future paths with the shortest travel time, the lowest travel time variability, or a combination, given the network conditions. A modified label-correcting algorithm is provided to solve for the shortest path that results from the proposed routing policies. Its running time is bounded by O(mn2), where m and n are the number of arcs and nodes, respectively, in the network. Since real-time traffic information is usually available with a certain level of accuracy, the proposed online routing policy integrates an existing information fusion model, which provides real-time short-term arc travel time distributions by considering information accuracy. Numerical experiments are used to demonstrate the performance of the proposed routing policies and algorithms, as well as the impacts of real-time information accuracy on the online stochastic routing.