Route Choice Probabilities Research Articles

Local detouredness: A new phenomenon for modelling route choice and traffic assignment

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.

Using traffic assignment models to assist Bayesian inference for origin–destination matrices

Estimation of traffic volumes between each origin and destination of travel is a standard practice in transport engineering. Commonly the available data constitute traffic counts at various locations on the network, supplemented by a survey-based prior estimate of mean origin–destination traffic volumes. Statistical inference in this type of network tomography problem is known to be challenging. Moreover, the difficulties are increased in practice by the presence of a large number of nuisance parameters corresponding to route choice probabilities, for which we have no direct prior information. Working in a Bayesian framework, we determine these parameters using a stochastic user equilibrium route choice model. We develop an MCMC algorithm for model fitting. This requires repeated computation of stochastic user equilibrium flows, and so we develop a computationally cheap emulator. Our methods are tested on numerical examples based on a section of the road network in the English city of Leicester.

E-scooter route choice in Germany – using stated preference data to investigate e-scooter route choice preferences

Electric kick scooter (e-scooter) have to use bicycle infrastructure in Germany. However, the illegal use of space exclusively dedicated to other modes (e.g. walking) is frequently observed. In order to understand why e-scooter users do (not) use the dedicated infrastructure it is essential to understand e-scooter route choice. The article presents results of an online survey and a stated choice experiment conducted to analyze route choice of e-scooter users in Germany. Data collection from 2020 finally resulted in 3,246 stated route decisions from 6,492 route alternatives responded by 424 participants. The analysis revealed that increasing travel time, number of intersections and high traffic volumes of motorized traffic along the route decrease route choice probability. In contrast, increasing the share of fine surface and bicycle infrastructure lead to an increase of route choice probability.

Incremental equilibrium assignment and applications to traffic network model

AbstractTo find a traffic assignment algorithm that is less complex than the current algorithms in terms of both methodology and implementation, the route‐based incremental equilibrium assignment (IEA) method is proposed. The IEA method iteratively performs incremental assignment and new origin‐destination (O‐D) demand extraction, where the new O‐D demand is obtained by aggregating the traffic flows from the routes whose travel times are greater than the shortest travel time. By extending the IEA method, the incremental stochastic user equilibrium and reliability‐based user equilibrium assignments are proposed to solve the stochastic user equilibrium (SUE) and reliability‐based user equilibrium (RUE) models, respectively. The traffic flow assignment and new O‐D demand extraction are carried out in terms of the route choice probability and the ratio of route flow to O‐D demand in the incremental stochastic user equilibrium assignment method; the redundant traffic flows from the non‐equilibrium routes are extracted. Unlike the IEA method, incremental reliability‐based user equilibrium assignment needs to derive both the mean and variance of the route travel time. The results from numerical experiments demonstrate the essential ideas and convergence performance of the proposed IEA algorithm and illustrate that IEA can be effectively extended to solve the SUE and RUE models.

A Dynamic Day-To-Day Departure Time and Route Choice Model for Bounded-Rational Individuals

Accurate prediction of travellers’ day-to-day departure time and route choice is critical in advanced traffic management systems. There have been several related works about route choice with the assumption that the departure time for individual travellers is known beforehand. With real-time traffic state information provided by navigation systems and previous historical experience, travellers will dynamically update their departure time, which is neglected in existing works. In this study, we aim to describe travellers’ spatial-temporary choice behaviour taking navigation information into account and propose a bounded-rational day-to-day dynamic learning and adjustment model. The new model contains three steps. First, the real-time navigation guidance on each discrete day is obtained, and the self-learned experience of travellers’ choices with navigation information is presented; then, the day-to-day revision process of the choices is derived to maximize departure and route choice prospect; next, by aggregating each individual’s behaviour and calculating route choice probability, a bounded-rational continuous day-to-day dynamic model is provided. Numerical experiments suggest that the proposed model converges to a spatial-temporal oscillating equilibrium not a fixed-point stable status, and the final equilibrium trend is different from classical user equilibrium. The findings of the study are helpful to improve the prediction accuracy of traffic state in urban street networks.

Day-to-day dynamics with advanced traveler information

This paper studies how the advanced traveler information affects the stability of the day-to-day flow evolution of a transportation system. Two scenarios are investigated regarding the types of information provided, where one type is the historical travel time and the other the forecasted travel time. Given the information, travelers are assumed to form their own perception/prediction on travel time and further choose the routes. The day-to-day dynamics under the two above-mentioned scenarios are formulated using both discrete-time and continuous-time models, and their respective local stability is analyzed. Findings from the discrete-time and continuous-time models are compared, which show that: (i) the discrete-time models behave in a more complex fashion than the continuous-time models, and (ii) the conclusions drawn from the discrete-time modeling and continuous-time modeling can be consistent, different or contradictory, which depends on the system parameters, network structure, the travel time functions and the route choice probability functions.

Verification and Optimization of Metro Fare Clearing Models Based on Travel Route Reconstruction

How to verify and optimize metro fare clearing models efficiently and accurately is a research focus in metro operations. Metro fare clearing models are mostly based on probability distributions. In such models, the normal distribution of travel time corresponding to the section probabilities is used to calculate the route choice probabilities of passengers on a multi-route metro network. By integrating the operating mileage proportions of each metro line operator and the corresponding route choice probabilities, the fare clearing proportions are calculated for all the operators of the metro network. To verify the accuracy of the fare clearing proportions, we propose a travel route reconstruction approach based on cell phone data acquisition technique. With wireless access point (AP) sensors installed at transfer stations, the unique medium access control (MAC) address of the smart phone with Wi-Fi function turned on is recorded and transmitted to a data analysis platform. After matching the MAC address information with time and location, the travel route of the smart phone user is reconstructed. Then, the parameters in the fare clearing model are verified and optimized according to the travel route choice probabilities. The proposed methodology is applied in Hangzhou metro network for experiment, and the metro fare clearing model is verified and modified by reconstructing the actual travel routs of the local passengers.

Analysis of driver’s choice behavior based on the combined model of utility-regret

In the context of increasing urban traffic trajectory data, based on large-scale urban traffic trajectory data, in the field of intelligent transportation, it has become an urgent need to analyze travelers’ routing behaviors and establish effective routing models to provide travelers with efficient and reasonable driving route recommendations. It is often the case that urban taxi groups has better route choice behavior than other travel group under the cost-benefit constraints. Therefore, providing route recommendations for other travelers according to the route choice habits of urban taxi group is a feasible solution to the above need. This paper starts with the utility maximization model and the stochastic regret minimization model, for the problem that the traveler cannot satisfy the premise of the complete rationality of the utility maximization model when choosing routes and the random regret minimization model ignores the traveler’s sensitivity to the original value of the alternative attribute, and improve the calculation formula of the regret value in the random regret minimization model. A route choice model under the utility-regret combination rule is proposed based on the combination of the utility model and the improved regret model, and then, based on the historical trajectory data of taxis in Xi’an, the RP data required by the model is extracted, and the parameters of the model are estimated and verified. The experimental results show that the route choice probability calculated by the model is closer to the route choice probability in the real scene than the simple utility maximization model or the simple random regret minimization model.

Path Size Logit route choice models: Issues with current models, a new internally consistent approach, and parameter estimation on a large-scale network with GPS data

Path Size Logit route choice models attempt to capture the correlation between routes by including correction terms within the route utility functions. This provides a convenient closed-form solution for implementation in traffic network models. The path size terms measure distinctiveness of routes; a route is penalised based on the number of other routes sharing its links, and the costs of those shared links. Typically, real road networks have many very long routes that should be considered unrealistic. Such unrealistic routes are problematic for the Path Size Logit (PSL) model because they negatively impact the choice probabilities of realistic routes when links are shared. The Generalised Path Size Logit (GPSL) model attempts to address this problem by weighting the contributions of routes to path size terms according to the ratio of route travel costs. However, the GPSL model is not internally consistent in how it defines routes as being unrealistic: the path size terms consider only travel cost, whereas the route choice probability relation considers disutility including the correction term.To solve these challenges, this paper formulates a new internally consistent Adaptive Path Size Logit (APSL) model wherein routes contribute to path size terms according to the ratio of route choice probabilities, ensuring that routes defined as unrealistic by the path size terms, are exactly those with very low choice probabilities. The APSL route choice probability relation is an implicit function, naturally expressed as a fixed-point problem. A proof is provided for the guaranteed existence of solutions, as well as conditions for the uniqueness of solutions. A Maximum Likelihood Estimation procedure is given for estimating the APSL model with tracked route observation data, and this procedure is investigated in a simulation study where it is shown it is generally possible to reproduce assumed true parameters. APSL is then estimated using real tracked route GPS data on a large-scale network, and results are compared with other PSL models..

A Dynamic Hierarchical Bayesian Model for the Estimation of day-to-day Origin-destination Flows in Transportation Networks

Estimation of origin-destination (OD) flows in transportation networks is a major step in transportation planning. We are interested in estimating OD flows given data on traffic link volumes over a sequence of days. We propose a dynamic hierarchical Bayesian model for the estimation of day-to-day OD flows. At the first level, we specify a dynamic Gaussian model which describes the evolution of OD flows over time. At the second level, we model the assignment of route flows given OD flows. Route choice probabilities are a function of user-predicted route costs, which depend on past user-experienced costs with a leaning parameter which controls the length of users memory. At the third level we model observed link volumes given route flows. Covariance matrices of OD flows and observed link volumes are modeled through variance functions. We develop a Metropolis-within-Gibbs algorithm to sample from the joint posterior distribution of OD flows, route flows, and parameters of the route choice model and the variance functions. Our model can be applied to both congested and uncongested networks and does not assume network equilibrium. We illustrate application of the model and sampling algorithm through numerical studies on two transportation networks from the literature. In a small test network, results indicate that OD flows and parameters may be identified given uninformative priors, while in a real-scale network OD flows and route choice parameters may be reasonably identified given prior knowledge on OD flows at the beginning of the planning horizon and their temporal variability.

Bayesian Inference on Dynamic Linear Models of Day-to-Day Origin-Destination Flows in Transportation Networks

Estimation of origin–destination (OD) demand plays a key role in successful transportation studies. In this paper, we consider the estimation of time-varying day-to-day OD flows given data on traffic volumes in a transportation network for a sequence of days. We propose a dynamic linear model (DLM) in order to represent the stochastic evolution of OD flows over time. DLMs are Bayesian state-space models which can capture non-stationarity. We take into account the hierarchical relationships between the distribution of OD flows among routes and the assignment of traffic volumes on links. Route choice probabilities are obtained through a utility model based on past route costs. We propose a Markov chain Monte Carlo algorithm, which integrates Gibbs sampling and a forward filtering backward sampling technique, in order to approximate the joint posterior distribution of mean OD flows and parameters of the route choice model. Our approach can be applied to congested networks and in the case when data are available on only a subset of links. We illustrate the application of our approach through simulated experiments on a test network from the literature.

Level-Change Stackelberg Games Model for the Combined Traffic Assignment–Signal Control Equilibrium on Networks

Combined traffic assignment–signal control equilibrium is usually integrated into a non-cooperative games model between the network authority and road users. Unlike a pure Wardropian equilibrium, in reality there may be both competition and cooperation between authority and users. Authority has always been regarded as the upper level in classical bi-level formulations, but this placement may increase the difficulty of obtaining a global optimal solution between authority and users. This paper proposes a level-change Stackelberg (LC Stackelberg) model that embraces both authority–user and user–authority formulations. The model is calibrated by a model predictive control (MPC) controller. A route-choice probability model is used to estimate flow burden on two parallel routes. Meanwhile, the difference of route-choice probability between the two parallel paths is regarded as the level-change threshold. A generalized autoregressive conditional heteroscedasticity (GJR-GARCH) model is used as a triggering function in the MPC controller to fulfill the level-change procedure. A modified wavelet neural network algorithm is used to seek the global optimal solution. Cournot, Stackelberg, and Monopoly, combined with a fixed-time control policy based on the Webster method, were chosen as benchmarks in a numerical example to test model validity. The results show that the LC Stackelberg model obtains the minimum total travel time compared with other models. Furthermore, the level-change between authority and users could also decrease route choice probability on one specific path, indicating the model’s potential application in urban networks.

Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering

The metro passenger route choice, influenced by both train schedule and time constraints, is important to metro operation and management. Smart card data (Automatic Fare Collection (AFC) data in metro system) including inbound and outbound swiping time are useful for analysis of the characteristics of passengers’ route choices in metro while they could not reflect the property of train schedule directly. Train schedule is used in this paper to trim smart card data through removing inbound and outbound walking time to/from platforms and waiting time. Thus, passengers’ pure travel time in accord with trains’ arrival and departure can be obtained. Synchronous clustering (SynC) algorithm is then applied to analyze these processed data to calculate passenger route choice probability. Finally, a case study was conducted to illustrate the effectiveness of the proposed algorithm. Results showed the proposed algorithm works well to analyze metro passenger route choice. It was shown that passenger route choice during both peak period and flat period could be clustered automatically, and noise data are isolated. The probability of route choice calculated through SynC algorithm can be used to revise traditional model results.

Application of the Combination of Random Utility Models (CoRUM) to route choice

The paper illustrates a route choice model based on the recently proposed Combination of Random Utility Models (CoRUM) model by Papola (2016). Specifically, the CoRUM specification with nested logit components, termed Combination of Nested Logits (CoNL), accounts for any correlation patterns amongst alternatives while keeping a closed-form expression for both probabilities and correlations. Thus, the paper illustrates a CoNL route choice model capable to target correlations between routes based on their topological overlapping, and characterized by a closed-form probability statement. The CoNL route choice model is operationalized by means of an algorithm providing automatically CoNL specification and corresponding route choice probabilities on a set of enumerated paths. Model performance is tested on various networks, with very satisfactory results.

A select link analysis method based on logit\u2013weibit hybrid model

Select link analysis provides information of where traffic comes from and goes to at selected links. This disaggregate information has wide applications in practice. The state-of-the-art planning software packages often adopt the user equilibrium (UE) model for select link analysis. However, empirical studies have repeatedly revealed that the stochastic user equilibrium model more accurately predicts observed mean and variance of choices than the UE model. This paper proposes an alternative select link analysis method by making use of the recently developed logit–weibit hybrid model, to alleviate the drawbacks of both logit and weibit models while keeping a closed-form route choice probability expression. To enhance the applicability in large-scale networks, Bell’s stochastic loading method originally developed for logit model is adapted to the hybrid model. The features of the proposed method are twofold: (1) unique O–D-specific link flow pattern and more plausible behavioral realism attributed to the hybrid route choice model and (2) applicability in large-scale networks due to the link-based stochastic loading method. An illustrative network example and a case study in a large-scale network are conducted to demonstrate the efficiency and effectiveness of the proposed select link analysis method as well as applications of O–D-specific link flow information. A visualization method is also proposed to enhance the understanding of O–D-specific link flow originally in the form of a matrix.

A constrained multinomial Probit route choice model in the metro network: Formulation, estimation and application.

Considering that metro network expansion brings us with more alternative routes, it is attractive to integrate the impacts of routes set and the interdependency among alternative routes on route choice probability into route choice modeling. Therefore, the formulation, estimation and application of a constrained multinomial probit (CMNP) route choice model in the metro network are carried out in this paper. The utility function is formulated as three components: the compensatory component is a function of influencing factors; the non-compensatory component measures the impacts of routes set on utility; following a multivariate normal distribution, the covariance of error component is structured into three parts, representing the correlation among routes, the transfer variance of route, and the unobserved variance respectively. Considering multidimensional integrals of the multivariate normal probability density function, the CMNP model is rewritten as Hierarchical Bayes formula and M-H sampling algorithm based Monte Carlo Markov Chain approach is constructed to estimate all parameters. Based on Guangzhou Metro data, reliable estimation results are gained. Furthermore, the proposed CMNP model also shows a good forecasting performance for the route choice probabilities calculation and a good application performance for transfer flow volume prediction.

Dynamic demand estimation and prediction for traffic urban networks adopting new data sources

Nowadays, new mobility information can be derived from advanced traffic surveillance systems that collect updated traffic measurements, both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities that academics and practitioners have only partially explored so far.The paper looks at some of these opportunities within the Dynamic Demand Estimation problem (DDEP). At first, data heterogeneity, accounting for different sets of data providing a wide spatial coverage, has been investigated for the benefit of off-line demand estimation. In an attempt to mimic the current urban networks monitoring, examples of complex real case applications are being reported where route travel times and route choice probabilities from probe vehicles are exploited together with common link traffic measurements.Subsequently, on-line detection of non-recurrent conditions is being recorded, adopting a sequential approach based on an extension of the Kalman Filter theory called Local Ensemble Transformed Kalman Filter (LETKF).Both the off-line and the on-line investigations adopt a simulation approach capable of capturing the highly nonlinear dependence between the travel demand and the traffic measurements through the use of dynamic traffic assignment models. Consequently, the possibility of using collected traffic information is enhanced, thus overcoming most of the limitations of current DDEP approaches found in the literature.

ESTIMATING PEDESTRIANS' ROUTE CHOICE PROBABILITY IN UNDERGROUND SPACE BASED ON OD MATRIX AND FLOW COUNT DATA

Underground spaces connected with railway stations gather a lot of pedestrians. Clarifying pedestrians' movement in underground space is important for flow control and evacuation planning. Based on a pedestrian traffic survey data and estimated OD matrix, this study aims to clarify the pedestrians' route choice probability in underground space. Through the definition of “state” and “transition process”, pedestrians' route choice model is constructed according to the utility of routes. Next, Absorbing Markov Process is adopted to solve the trip assignment in the underground pedestrian network and build the functional relation between estimated flow counts and obtained data. Through minimizing the difference between estimated and observed flow counts, unknown variables are calibrated. Finally, the influential spatial motion attributes are examined through linear regression analysis.

Constrained multinomial Probit route choice modeling for passengers in large-scaled metro networks in China

Abstract Considering that a large scaled metro network provides the opportunity of multiple route choice, it is necessary to consider integrating the impacts of routes set and the interdependency among alternative routes on route choice probability into route choice modeling. Therefore, a constrained multinomial probit (CMNP) route choice model in the large scaled metro network is proposed in this paper. The utility function is formulated to be composed of the following three components: the compensatory component is a linear function of level of service variables and route direction measurement, such as in-vehicle travel time, number of transfers, transfer time, congestion level and revised angular cost; the non-compensatory component represented by the logarithm function of a binary probit equation denoting the relationship between the constrained attributes and the corresponding thresholds measures the impact of considered probability of one route on the route’s utility; following a multivariate normal distribution, the covariance of the error component is structured into two parts, that is, the part measuring the correlation among routes, and the part denoting the unobserved variance distributed independently by route. Based on the surveyed revealed preference data in the Guangzhou Metro system, the estimations show that the proposed CMNP model shows the superiority of goodness- of-fit to data over traditional models. Meanwhile, the results also indicate that the non-compensatory component in the CMNP model works well to explain the impact of routes set on route choice probability.

Why do drivers change route? effect of graphical route information panels

Studies that explore drivers’ decision behaviour under the graphical route information panels (GRIPs) via the discrete choice models have rarely been reported. In this paper, drivers’ route choice response to the GRIPs is quantitatively analysed by using the discrete choice models through a case study of a real-life GRIP in Shanghai, China. A logit model for predicting the route choice probabilities is developed to capture the relationship between the route choice probability, the driver attributes and the GRIP messages, respectively. The modelling results show that the drivers who have a larger annual driving mileage, access the traffic information via the electronic message signs in daily life highly value the GRIPs and drive their car mainly for the commuting purpose are more likely to divert from the original route to the alternate route under the GRIP; a driver's perceived delay of the original route has a positive effect on his diversion decision under the GRIP; there exist differences in the GRIP response behaviour; the drivers are more likely to divert when the GRIP displays ‘red’ to indicate severe congestion on the original route; the female drivers are more sensitive to ‘red’ and more likely to divert.