Route Choice Process Research Articles

Causal analysis of flight en route inefficiency

En route inefficiency is measured in terms of extra distance flown by an aircraft, above a benchmark distance that relates to the theoretical shortest distance route (great circle route). In this paper, we have explored causal relations among en route inefficiency with multiple identified sources: convective weather, wind, miles-in-trail (MIT) restrictions, airspace flow programs (AFPs) and special activity airspace (SAA). We propose two mechanisms – strategic route choice and tactical reroute – to ascribe flight en route inefficiency to these factors. In our framework, we first propose an efficient trajectory clustering algorithm to identify nominal routes that reveal both air traffic flow patterns and air route structures in the national airspace system. Second, we develop a tree-based searching algorithm that matches different causal factors to the identified nominal routes in a 4-dimensional manner. Third, we employ a discrete choice modeling framework to quantitatively understand the flight route choice process, and establish a linear model to understand the tactical reroute process. Finally, we estimate the contributions of the identified causal factors to flight inefficiency through counterfactual analysis. Numerical experiments based on 8 representative airport pairs suggest strong negative impact of convection and headwind on route choice process, while MIT, AFP and SAA only demonstrate negative effects on some pairs. We have also shown that the contributions of convective weather, wind, traffic management initiatives, and SAA are up to 7.1%, 14.0%, 4.4%, 21.9%, respectively.

Modelling Route Choice in Crowd Evacuation on Passenger Ships

This paper proposes an agent-based simulation model with route choice process to predict the crowd behaviours and evaluate the evacuation safety on passenger ships. The model focuses on the behaviours of two common types of passengers that are not typically accounted for during most evacuation analyses, namely, passengers who are not familiar with the ship layout and passengers who have family members or friends with them. In the proposed model, a marker concept is introduced to represent critical routing points of the layout and passenger agents make a route choice based on their surroundings and characteristics instead of just following the shortest routes. The simulation model is tested by two small but targeted scenarios and one comprehensive scenario on a ship deck. For ship designers, a more realistic evacuation time is provided to better assess the evacuation performance of a ship, and a heat map of crowd density is presented to identify possible bottleneck areas.

A Multi-Period Tradable Credit Scheme Incorporating Interest Rate and Traveler Value-of-Time Heterogeneity to Manage Traffic System Emissions

This study focuses on a tradable credit scheme (TCS)-based multi-period equilibrium modeling framework to address the planning problem of a central authority which seeks to minimize the vehicular emissions in a traffic network over a planning horizon (PH). In this context, the multi-period TCS (MPTCS) equilibrium conditions consist of traffic and market equilibrium conditions. To develop an effective TCS design in practice, this study factors the heterogeneity in travelers' value of time (VOT) to enable realism in capturing the traffic equilibrium conditions, and the interest rate to reflect market realism. The VOT is an important factor in the route choice process as travelers tradeoff the credit consumption and travel time costs of each route. Further, travelers decide between selling or transferring unused credits across periods based on the dynamic interest rates over the PH as they can accrue interest by selling credits in the market. The study investigates the existence and uniqueness of multi-period equilibrium credit prices, aggregate link flows and travel demand rates. Then, the equilibrium credit prices under the MPTCS are analyzed and it is demonstrated that while credit price volatility increases with increasing interest rates, it can be dampened by the ability of travelers to transfer credits under the MPTCS. Finally, a system optimal (SO) MPTCS is designed to determine the TCS parameters (credit allocation and charging schemes) that minimize vehicular emissions in a traffic network over the PH. The study insights suggest that the SO design of MPTCS provides a capability to manage network vehicular emissions over the PH. Further, they suggest that if VOT is not factored, the TCS would be socially inequitable, and ignoring interest rates creates inefficiencies in achieving desired emissions levels.

How do users choose their routes in public transport? The effect of individual profile and contextual factors

The aim of the present study was to better understand how public transport users make their choice of route, in order to favor the use of public transport (henceforth PT) in large cities. Based on decision-making theories, a classical choice paradigm (Slovic, 1975), and recent findings in psychology (Chowdhury & Ceder, 2013; Grison et al., 2016) we developed a new method to investigate the effect of contextual and individual factors on PT route choices. We proposed to sixty PT users realistic forced choices between two PT routes that differed in affective (level of physical comfort) and instrumental (number of transport modes) attributes. We also varied the context of the decision (long or short route; route to go to work, to a leisure activity, etc.), and we recorded various individual characteristics (age, sex, attitude towards PT, habits, etc.). Our results highlight that: the comfort of the route is preferred to the number of transport modes, especially for long trips; the choice of the comfortable alternative or the one with only one transport mode depends on user characteristics; the length of the trip and habits are the most important variables in the decision, but attitudes also seem to play a major role. Our study furthers current knowledge about the psychological process of PT route choice in light with multi-attribute choices theories, and provides new insights that can contribute to improving current large city route-planning aids.

Analyzing multiday route choice behavior of commuters using GPS data

In this study, accurate global position system and geographic information system data were employed to reveal multiday routes people used and to study multiday route choice behavior for the same origin–destination trips, from home to work. A new way of thinking about route choice modeling is provided in this study. Travelers are classified into three kinds based on the deviation between actual routes and the shortest travel time paths. Based on the classification, a two-stage route choice process is proposed, in which the first step is to classify the travelers and the second one is to model route choice behavior. After analyzing the characteristics of different types of travelers, an artificial neural network was adopted to classify travelers and model route choice behavior. An empirical study using global position systems data collected in Minneapolis–St Paul metropolitan area was carried out. It finds that most travelers follow the same route during commute trips on successive days. And different types of travelers have a significant difference in route choice property. The modeling results indicate that neural network framework can classify travelers and model route choice well.

A Study On Drivers' Route Choice Under The Influence Of ATIS Accuracy

This paper investigates the effects of ATIS (Advanced Traveler Information Systems) accuracy on drivers' compliance with descriptive information and route choice behavior. The approach adopted here examines drivers' compliance with ATIS and the subsequent route choice using a nested logit model. Compliance is modeled as a binary logit model which is then used as an upper branch to the route choice process. Stated Preference (SP) methods are used to collect data on drivers' behavior. The questionnaire incorporated the factors affecting drivers' perception of information accuracy such as travel time error average and variability. The results showed that not only does ATIS accuracy affect drivers' compliance, but it also has an impact on the reliability of each route. Attitudes toward risk were also found important in the decision making process.

Random Parameter Nested Logit Model for Combined Departure Time and Route Choice

A driver’s daily commute involves making three types of decisions: choice of departure time, route choice and mode choice. The objective of this paper was to investigate potential impacts of implementing variable congestion charging on driver's daily commute choice. Additionally, this study focused on the departure time and route choice process and investigated Random Parameter Nested Logit model (RPNL) for the combined departure time and route choice. Software R was used for stated choice experiment design, while software BIOGEME was used for model estimation based on the collected data. Compared with the basic Multinomial Logit model (MNL), the estimation results indicated that Random Parameter Nested Logit model (RPNL) improved the model performance, due to considering inter-alternative correlations as well as commuters’ heterogeneity. The combined choice models were utilized to present commuters’ trade-offs among commute time, monetary cost and schedule delay. The results showed that under the situation without flexible work patterns, the disutility of late arrival and early departure were high, which resulted in less attractiveness for commuters to switch their departure time and further weaken the performance of implementing congestion charging. Therefore, in order to enhance the performance of a congestion charging policy for relieving traffic congestion in a community, elastic work patterns as well as other complimentary measures should be introduced in parallel.

Calibration of a Pedestrian Route Choice Model with a Basis in Friction Forces

This paper presents a pedestrian route choice model and its calibration with real data. The model explicitly represents interaction between pedestrians as an impedance force influences their route choice. This model approach was inspired by friction force equations and considered efforts by pedestrians to avoid passing near other pedestrians with high relative velocity. The route choice process was a function of impedance force and route length. A social force model was used to model pedestrian walking behavior. The calibration had its basis in data acquired from a real experiment developed in a simplified network. Data collection had its basis in video analysis. The paper presents and discusses results from the calibration processes. The model presented in this paper differs from other pedestrian route choice models because it seamlessly incorporates a pedestrian social force model into the route choice decision process.

Pedestrian route choice model based on friction forces

This paper presents a pedestrian route choice model devised to represent the influence of the impedance generated by other pedestrians on the route choice process. This model is inspired by friction force equations, and considers that pedestrians avoid passing near other pedestrians with high relative velocity. The route choice process is based on a weighting of the impedance generated by pedestrians and the path length. A social force model was used to model pedestrian walking behavior. The model is able to reproduce emergent behavior among agents, allowing the assumption that the friction equations may provide a suitable approach to route choice behavior and can also be used as an indirect measure of pedestrian delay.

Dynamic Route Choice Based on Prospect Theory

Traveller route choice behaviour is influenced by many uncertain factors, which include both outer stochastic factors from road network and inner factors from traveller psychology. Traveller route choice behaviour is dynamic in practical travel process, namely traveller can change route on the way. Considered the characteristics of bounded rationality in human decision making process and change of route choice on the way, a dynamic route choice model is established based on prospect theory, and the method of dynamic route choice which is more suitable to human thinking habits and actual travel characteristics is proposed in this paper. The method allows the traveller to adjust the route according to road situation at any time. Decision making process reflects that the influence of traveller reference point on the decision result. Reference point changes with the situation of road sections passed. The examples are given to verify the validity and explain application of the method. The result shows that traveller can change route on the way when he meet congestion on the section passed, and if traveller's psychological expected time can not be met, he will choose risky route, by which the probability of congestion is higher. The method describes traveller route choice behaviour process better and reflects the true situation of road network. It is beneficial to predict traveller behaviours and changes of traffic network situation, and have certain guiding significance for traffic network planning and traffic intelligent control.

Tradable credit scheme for mobility management considering travelers’ loss aversion

Under a given tradable credit scheme, travelers’ loss aversion behavior for credit charging during the route choice process is studied. A disutility function of loss aversion is applied to approach travelers’ different attitudes towards credit loss and gain, and the transaction costs of buying and selling credits are also incorporated in the function. The user equilibrium (UE) and market equilibrium (ME) conditions considering loss aversion effects are formulated into a variational inequality (VI) problem. Analyses demonstrate that the system optimum (SO) credit scheme does not always exist. A proposition is further presented to guarantee its existence.

Modeling Driver Behavior in a road network with route choice based on real time traffic information

Intelligent Transportation Systems (ITS) applications require a thorough understanding of drivers' route choice behavior in a complex network under real-time information. The purpose of this paper is to describe and model driver route choice behavior in a road network based on real time traffic information at the disaggregate individual level and from a psychological decision-making process perspective. The framework of routing choice and driver dynamic route choice behavior model that uses concepts from Decision Field Theory (DFT) and Bayesian belief network (BBN) is proposed. A real-time planning algorithm for route choice processes is discussed in great detail. Using this algorithm, a driver develops his route dynamically until he reaches his destination. The simulation results show that the combination of DFT and BBN can effectively describe the driver's travel dynamics behavior . DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4466 Full Text: PDF

Accounting for Travel Time Reliability and Trip Purpose in an Agent-Based Approach to Toll Pricing with Dynamic Feedback Control

Congestion pricing is an effective management policy to reduce traffic congestion on freeways. This study accounted for the travel time difference and reliability on managed and general-purpose lanes in a modified approach to determining toll rates recently developed by others. The original approach was modified by developing an agent-based toll pricing strategy that used dynamic feedback control and accounted for trip purpose, travel time reliability (TTR), and level of income to maximize toll revenue while maintaining a minimum desired level of service on managed lanes. An external module was developed to execute the modified strategy in VISSIM, which exported the traffic data generated to an Excel spreadsheet in real time to calculate TTR, used in the route choice process. Agent-based modeling simulated drivers’ learning process and estimation of TTR from their previous commuting experience. A numerical example illustrated the modified strategy. The simulation results confirmed that, under high traffic demand, drivers with an urgent trip purpose had the highest probability of choosing managed lanes and that travel time on those lanes was more reliable than on general-purpose lanes. The modified strategy, the strategy currently deployed on I-95 express lanes, and the original strategy were compared. The modified one had a steadier increase in toll rate, significantly higher toll revenue, and speeds higher than 45 mph more frequently, compared with the current strategy. Furthermore, the modified approach was more realistic, accounted for TTR in route choice, and generated higher toll revenue under heavy traffic demand than did the original strategy.

Assessing the Importance of Vehicle Type for the Implementation of Eco-routing Systems

Nowadays, several methods to promote a more sustainable distribution of traffic flows are available. In response to rising energy costs and increased environmental concerns, eco-friendly route choices can be provided individually by means of smart navigation tools that allow several vehicle routing options designed to minimize air pollutant emissions and fuel consumption. Simultaneously, the use of intelligent road pricing systems and the use of variable message signs can change the route choice process of drivers (and thus network equilibrium), by varying the perceived attributes of competing routes. However, so far too little attention has been paid to the fact that the eco-friendliness of various routes may change, depending on vehicle characteristics which may cause problems on the efficiency of these systems. This issue has been empirically addressed in this research, using a database of more than 13,330km of GPS data in six different Origin-Destination (OD) pairs and 9 different routes. Simultaneously, two different approaches for estimating (CO2, HC, CO, NOX) emissions were tested: a) second-by-second vehicle dynamics, using the Vehicle Specific Power (VSP) instantaneous model; and b) route average speed using the EMEP/EEA methodology. The results show that depending on the characteristics of the routes associated with a certain OD pair, the eco-friendly route may differ according to the vehicle model and the emissions estimation method. Innovative approaches to provide accurate emissions and eco-routing information are needed.

Day-to-day Traffic Dynamics: Laboratory-like Experiment on Route Choice and Route Switching in a Simple Network with Limited Feedback Information

An experimental analysis of day-to-day route choice dynamics in a simple three-route network is described in this paper. A laboratory-like experiment involving thirty subjects was conducted over a 50-period time span. Participants acted non- cooperatively and at each period received only feedback information on travel times of chosen routes. The results indicate that the day-to-day route choice process was characterized by a high degree of volatility, and that User Equilibrium, even though reached occasionally, did not persist as a steady state of the network. However, the detection of statistically significant trends underlying the evolution of network state descriptors suggests the possibility that equilibrium conditions might have been approached over an appropriately extended time horizon. Individual route switching frequency was found to be significantly correlated to average experienced travel time, while both variables did not exhibit any significant relationship to personal characteristics of participants. Finally, observations collected in the experiment were used to derive estimates of behavioral parameters of a deterministic process model of day-to-day route choice dynamics.

Spatial Environmental Analysis on the Effects of a New Navigation System for Freight Transport

Considerable attention has been paid to traffic pollution modeling for commercial vehicles due to their high amount of nitrogen compounds, particulate matter and greenhouse gas emissions. Conventional navigation systems for passenger cars enable the route choice process. However, commercial route guidance systems are not targeted to trucks specifically. Recently, a new navigation system has been designed to match the specific requirements of commercial vehicles. The purpose of this paper is to evaluate the effects of such a navigation system on the spatial environment. The major emissions varied by nitrogen compounds and particulate matters are approximated for hot emissions. Emissions on the fastest route and the suggested route followed by drivers are investigated respectively. Spatial analysis is used to investigate the influence on built-up areas along the routes, and a regression analysis is conducted to specify the effects of the new navigation information on total emissions. It is found that the new navigation system has a significant positive effect on the improvement of traffic emissions. The routes suggested by the new navigation system lead to less impact on the built-up areas than the shortest paths.

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.

Why Choose the New I-35W Mississippi River Bridge?

On September 18th 2008, a replacement for the previously collapsed I-35W bridge opened to the public. Consequently, travelers were once again confronted with the opportunity to find better alternatives. The traffic pattern of the Minneapolis road network was likely to readjust, because of the new link addition. However, questions arise about the possible reasons (or components in the route choice process) that are likely to influence travelers crossing the Mississippi, who had to choose among the bridge options, includingthe new I-35W bridge. Using GPS data and web-based survey collected both before and after the replacement bridge opened, a bridge choice model is estimated using weighted-least squares logit. In this way the proportion of I-35W trips can be estimated depending on the assigned values of the explanatory variables, which include: statistical measures of the travel time distribution experienced by the subjects, alternative diversity, and others. The results showed that travel time savings and reliability were the main reasons for choosing the new I-35W bridge.

MINIMIZING EXPOSURE RISK AND TRAVEL TIMES OF HAZARDOUS MATERIAL TRANSPORTATION IN URBAN AREAS

A HAZMAT vehicle routing problem considering multi-objective requirements and a meta-heuristic solution algorithm is presented in this paper. In contrast to existing studies, an attempt has been made to carry out routing and route choice processes together as a single step process. Pareto-optimal concept using non-dominated paths are utilized, dealing with multiple objectives during both of these processes. An application of the proposed algorithm for solving a test HAZMAT vehicle routing problem is also presented. The set of pareto-optimal solutions obtained provides a number of economical and safe routing solutions, identifying the alternative routing choices in decision-making.

Reliable stochastic design of road network systems

This paper investigates the continuous version of the stochastic Network Design Problem (NDP) with reliability requirements. The problem is considered as a two-stage Stackelberg game with complete information and is formulated as a stochastic bi-level programming problem, which is extended to include reliability as well as physical and budget constraints. The estimation procedure combines the use of Monte Carlo simulation for modelling the stochastic nature of the system variables with a Genetic Algorithm (GA), for treating the complexity of this new formulation. The computational experience obtained from a test road network application demonstrates the ability of the proposed methodology to address the need for incorporating reliability requirements and stochasticity into the various system components in the design process. The results can provide useful insight into the evaluation of alternative reliable network capacity improvement plans under the effect of uncertainty on the demand, supply and route choice process of travellers.