Perceived Travel Time Research Articles

Over-reporting vs. overreacting: Commuters’ perceptions of travel times

In a large-scale, real-life peak avoidance experiment, we asked participants to provide estimates of their average in-vehicle travel time during their morning commute. After comparing the reported travel times with the actual corresponding travel times, we found that the average travel times were overstated by a factor of 1.5. We showed that driver- and link-specific characteristics partially explained these exaggerations. Using the stated and revealed preference data, we investigated whether the driver-specific reporting errors were consistent with the drivers’ scheduling behaviors in reality and in hypothetical choice experiments. In both cases, we found no robust evidence that drivers behave as if they misperceive travel times to a similar extent as those they misreported, thereby implying that the reported travel times did not represent the actual or perceived travel times in a truthful manner. The results of this study suggest that caution should be recommended when reported travel time data are used in an uncritical manner during transport research and when determining policy.

A Day-to-day Route Choice Model based on Travellers’ Behavioural Characteristics

This paper proposes a day-to-day route choice model based on travellers’ behavioural characteristics. The model is able to reflect travellers’ route choice in one day and describes the diversity of route choice behaviours. First, characteristics of the day- to-day route choice and travellers’ bounded rationality are analysed. A loop architecture based on the evolution of travellers’ daily choices is presented; deliberate route choice and habitual route choice in the architecture are discussed. A model is then proposed based on the theoretical analysis. In this model, travellers’ behavioural characteristics in the day-to-day route choice are considered; i.e., forgetting previous travel times, risk attitude, habit effect, traveller's amount of attention on the route choice being made, perception of actual travel time, etc. Results of a numerical experiment show that the tendencies of the simulated travellers’ choices are reasonable and similar with that in reality, and that the model is able to reproduce travellers’ route choice with more behavioural characteristics.

Can We Model Driver Perceptions? An In-Situ Experiment in Real-World Conditions

It is clear that perceptions play a significant role in traveler decisions. Consequently, traveler perceptions are a corner stone in the feasibility of traveler information systems; for traveler information systems are only valuable if the drivers are incapable of accurately acquiring the provided information on their own, and if the provided information is relevant for the drivers' decision criteria. Accuracy of traveler perceptions has been repeatedly researched in public transportation, and has been found to vary according to different reasons. However, in spite of the clear significance of traveler perceptions, minimal effort has been put into modeling it. Almost all travel behavior models are based on traveler experiences, which are assumed to reflect traveler perceptions via the addition of some random error component. This works introduces an alternative approach: instead of adding an error component to represent driver perceptions, it proposes to model driver perceptions. This work is based on a real-world route choice experiment of a sample of 20 drivers who made more than 2,000 real-world route choices. Each of the drivers' experiences, perceptions, and choices were recorded, analyzed and cross examined. The paper demonstrates that: i) driver experiences are different from driver perceptions, ii) driver perceptions explain driver choices better than driver experiences, iii) it is possible to model and predict driver perceptions of travel distance, time and speed.

Day-to-day travel time perception modeling using an adaptive-network-based fuzzy inference system (ANFIS)

Travel time perception and learning play a central role in the modeling of day-to-day travel choice dynamics in traffic networks and have attracted the attention of many researchers, specifically for the analysis and operation of intelligent transportation systems and travel demand management scenarios. In this paper, a fuzzy learning model is proposed to capture the mechanism by which travelers update their travel time perceptions from one day to the next, taking into account their experienced travel times. To capture travelers’ mental representations of uncertain travel time involving imprecision and uncertainty, a combined artificial neural network and fuzzy logic (neuro-fuzzy) architecture called adaptive-network-based fuzzy inference system is employed. This framework, which utilizes a set of fuzzy if–then rules, can serve as a basis for modeling the qualitative sides of travelers’ knowledge and reasoning processes. From the output of this study, the results of our laboratory-like experiment provide a good fit to the stated data of travelers’ behavior, and may reflect the fact that the neuro-fuzzy approach can be considered a promising method in learning and perception updating models. Finally, the proposed learning model is embedded in a microscopic event-based simulation framework to evaluate its credibility within a day-to-day behavior of the traffic network. The results of the simulation, which converge to the equilibrium state of the test network, are finally presented, implying that the proposed perception updating model operates properly.

Travel Time Reliability Using the Hasofer–Lind–Rackwitz–Fiessler Algorithm and Kernel Density Estimation

Travel time reliability has emerged as an indicator of roadway performance. Estimation of travel time distribution is an important starting input for measuring travel time reliability. This study used kernel density estimation to estimate travel time distribution. The Hasofer–Lind–Rackwitz–Fiessler algorithm, widely used in the field of reliability engineering, was used in this work to compute the reliability index of a system based on its previous performance. The computing procedure for travel time reliability of corridors on a freeway was introduced, then network travel time reliability was developed. Given probability distributions estimated by the kernel density estimation technique and an anticipated travel time from travelers, the two equations of corridor and network travel time reliability can be used to address the question, “How reliable is my perceived travel time?” The definition of travel time reliability was in the sense of on-time performance, and this study was conducted from the perspective of travelers. The major advantages of the proposed method are as follows: ( a) it demonstrates an alternative way to estimate travel time distributions when the choice of probability distribution family is still uncertain and ( b) it shows its flexibility for application to levels of roadways (e.g., individual roadway segment or network). A user-defined anticipated travel time can be input, and travelers can use the computed travel time reliability information to plan their trips so that they can better manage trip time, reduce costs, and avoid frustration.

Network Structure and Travel Time Perception

The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time.

A Harmony Search Algorithm approach for optimizing traffic signal timings

In this study, a bi-level formulation is presented for solving the Equilibrium Network Design Problem (ENDP). The optimisation of the signal timing has been carried out at the upper-level using the Harmony Search Algorithm (HSA), whilst the traffic assignment has been carried out through the Path Flow Estimator (PFE) at the lower level. The results of HSA have been first compared with those obtained using the Genetic Algorithm, and the Hill Climbing on a two-junction network for a fixed set of link flows. Secondly, the HSA with PFE has been applied to the medium-sized network to show the applicability of the proposed algorithm in solving the ENDP. Additionally, in order to test the sensitivity of perceived travel time error, we have used the HSA with PFE with various level of perceived travel time. The results showed that the proposed method is quite simple and efficient in solving the ENDP.

Overreporting vs. Overreacting: Commuters' Perceptions of Travel Times

Participants of a large-scale, real-life peak avoidance experiment have been asked to provide estimates of their average in-vehicle travel times for their morning commute. Comparing these reported travel times to the corresponding actual travel times, we find that travel times are overstated by a factor of 1.5 on average. We test to which extent driver- and link-specific characteristics explain the overstating. Subsequently, we investigate whether the driver-specific reporting errors are consistent with the drivers' scheduling behavior in reality as well as in hypothetical choice experiments. For neither case, we find robust evidence that drivers behave as if they misperceived travel times to a similar extent as they misreported them. These results imply that reported travel times are neither an appropriate measure for representing actual nor perceived travel times, and are thus a strong caveat against the uncritical use of reported travel time data in research and policy.

Drivers' Perception of Route Alternatives as Indicator for the Indifference Band

Although travel time is probably one of the most important attributes in route choice, the shortest time route is often not the preferred route, according to several studies in the literature. This study tries to explain this finding by testing the hypothesis that choice makers may be able to estimate travel times correctly for routes that they prefer but are biased against alternatives even if these are faster. For a few choice sets of routes in the city of Enschede, Netherlands, respondents were asked to choose a route and provide their estimated travel times for both the preferred and the alternative routes. These travel times were then compared with actual travel times from a license plate study. The comparison confirmed the hypothesis. For chosen routes, perceived travel times correspond quite well with actual travel times on average, whereas for routes not chosen, perceived travel times are overestimated by 3 to 4 min on average. These results show that drivers are not able or do not want to evaluate routes objectively. This finding implies that within an indifference band of route delay or travel time inequality of on average 3 to 4 min, drivers are probably not willing to alter their route choice, even if the traffic situation, induced, for example, by traffic management measures, changes in a negative way for their preferred route.

Reliability-Based Marginal Cost Pricing Problem Case with Both Demand Uncertainty and Travelers’ Perception Errors

Focusing on the first-best marginal cost pricing (MCP) in a stochastic network with both travel demand uncertainty and stochastic perception errors within the travelers’ route choice decision processes, this paper develops a perceived risk-based stochastic network marginal cost pricing (PRSN-MCP) model. Numerical examples based on an integrated method combining the moment analysis approach, the fitting distribution method, and the reliability measures are also provided to demonstrate the importance and properties of the proposed model. The main finding is that ignoring the effect of travel time reliability and travelers’ perception errors may significantly reduce the performance of the first-best MCP tolls, especially under high travelers’ confidence and network congestion levels. The analysis result could also enhance our understanding of (1) the effect of stochastic perception error (SPE) on the perceived travel time distribution and the components of road toll; (2) the effect of road toll on the actual travel time distribution and its reliability measures; (3) the effect of road toll on the total network travel time distribution and its statistics; and (4) the effect of travel demand level and the value of reliability (VoR) level on the components of road toll.

A Day-to-day route choice model based on drivers’ past experience

The objective of this study is to develop a non UE (User Equilibrium)-based traffic assignment model which is able to be applied to a real size network. Over the last few decades, the UE principle has been used as a standard route choice principle. However, it also has received considerable criticism since it is difficult to fully describe the route choice behavior of real drivers. The real drivers select a route based on their cumulated experiences, and their perceived travel times are also not deterministic values, but probabilistic values which reflect the various uncertainties existing in the network. If a driver frequently uses a specific route, then the driver has better information on that route than other routes. As a result, a marginal superiority of other paths might not make the driver quit using the frequently selected path. Accordingly, a network traffic pattern is not so sensitive to the change of travel environment and drivers tend to select routes stably and habitually. In addition, the cumulated information in drivers’ minds and the travel experiences are also important to develop traffic operation and management schemes. However, a UE-based traffic assignment model cannot take into account the previous travel experiences and network uncertainties. Therefore, the route choice in the UE-based model might be over-sensitive to the change of travel environment in the network or occasionally forecasted incorrectly because of the absence of consideration for previous travel experiences and perception limit. In order to overcome these problems, the authors developed an agent-based traffic assignment model while making use of “reference point” theory in the field of psychology and “bounded rationality” used in decision making theory. In the developed model, travel time is modeled not as a deterministic value, but as the concept of probability density function in order to reflect the network uncertainties with which the real drivers face in the real world. In addition, the density function is linearly simplified in order to improve applicability to the real-size transportation network problems.

Assessing the effects of stochastic perception error under travel time variability

Perceived mean-excess travel time is a new risk-averse route choice criterion recently proposed to simultaneously consider both stochastic perception error and travel time variability when making route choice decisions under uncertainty. The stochastic perception error is conditionally dependent on the actual travel time distribution, which is different from the deterministic perception error used in the traditional logit model. In this paper, we investigate the effects of stochastic perception error at three levels: (1) individual perceived travel time distribution and its connection to the classification by types of travelers and trip purposes, (2) route choice decisions (in terms of equilibrium flows and perceived mean-excess travel times), and (3) network performance measure (in terms of the total travel time distribution and its statistics). In all three levels, a curve fitting method is adopted to estimate the whole distribution of interest. Numerical examples are also provided to illustrate and visualize the above analyses. The graphical illustrations allow for intuitive interpretation of the effects of stochastic perception error at different levels. The analysis results could enhance the understanding of route choice behaviors under both (subjective) stochastic perception error and (objective) travel time uncertainty. Some suggestions are also provided for behavior data collection and behavioral modeling.

Stochastic Traffic Equilibrium Based on Travel Time Robust Reliability

An assumption that pervades the current reliable traffic equilibrium problem is that probability distributions of the origin-destination (O-D) demand or/and link capacities are known explicitly. However, these distributions are difficult to be accurately obtained. This paper relaxes this assumption. It only needs to know the first m moments of travel demand (where m is a positive integer associated with the formulation of link cost function), and then applies two worse-case Value-at-Risk (WVaR) and Conditional value-at-risk (CVaR) risk measures to define robust percentile travel time (RPTT) and robust mean-excess travel time (RMETT) and prove that this two kinds travel time is equal under general distribution. By incorporating the defined travel time and travelers' perception error, the robust percentile stochastic user equilibrium (RPSUE) or robust mean-excess stochastic traffic equilibrium model (RMESTE) is proposed, which is formulated as an equivalent route-based variational inequality. Conditions are established guaranteeing existence of this equilibrium. A heuristic solution problem is introduced to solve the variational inequalities problem. A numerical example is used to illustrate the applications of the proposed model and the solution algorithm.

A bi-objective cyclist route choice model

It is widely acknowledged that cyclists choose their route differently to drivers of private vehicles. The route choice decision of commuter drivers is often modelled with one objective, to reduce their generalised travel cost, which is a monetary value representing the combined travel time and vehicle operating cost. Commuter cyclists, on the other hand, usually have multiple incommensurable objectives when choosing their route: the travel time and the suitability of a route. By suitability we mean non-subjective factors that characterise the suitability of a route for cycling, including safety, traffic volumes, traffic speeds, presence of bicycle lanes, whether the terrain is flat or hilly, etc. While these incommensurable objectives are difficult to be combined into a single objective, it is also important to take into account that each individual cyclist may prioritise differently between travel time and suitability when they choose a route.This paper proposes a novel model to determine the route choice set of commuter cyclists by formulating a bi-objective routing problem. The two objectives considered are travel time and suitability of a route for cycling. Rather than determining a single route for a cyclist, we determine a choice set of optimal alternative routes (efficient routes) from which a cyclist may select one according to their personal preference depending on their perception of travel time versus other route choice criteria considered in the suitability index. This method is then implemented in a case study in Auckland, New Zealand.The study provides a starting point for the trip assignment of cyclists, and with further research, the bi-objective routing model developed can be applied to create a complete travel demand forecast model for cycle trips. We also suggest the application of the developed methodology as an algorithm in an interactive route finder to suggest efficient route choices at different levels of suitability to cyclists and potential cyclists.

A Long-Distance Travel Demand Model for Europe

In Europe, approximately 50% of all passenger kilometres come from trips beyond 100 km according to matrices developed in the TRANSTOOLS project. This accounts for an even larger share of CO2 emissions due to a higher modal share of air transport. Therefore long-distance trips are increasingly relevant from a political and environmental point of view. The paper presents the first tour-based long-distance travel demand model for passenger trips in and between 42 European countries. The model is part of a new European transport model developed for the European Commission, the TRANSTOOLS II model, and will serve as an important tool for transport policy analysis at a European level. The model is formulated as a nested logit model and estimated based on travel diary data with segmentation into business, private, and holiday trips. We analyse the estimation results and present elasticities for a number of different level-ofservice variables. The results suggest that the perception of both travel time and cost varies with journey length in a non-linear way. For car drivers and car passengers, elasticities increase with the length of the journey, whereas the opposite is true for rail, bus, and air passengers – a fact that reflects a change in substitutability. Moreover, elasticities differ significantly by trip purpose with private trips having the highest and holiday trips the lowest elasticities.

The Impact of Traffic Information on Drivers’ Day-to-Day Route Choice Behavior

The influence of different traffic information on drivers’ day-to-day route choice behavior based on microscopic simulation is investigated. Firstly, it is assumed that drivers select routes in terms of drivers’ perceived travel time on routes. Consequently, the route choice model is developed. Then, updating the drivers’ perceived travel time on routes is modeled in three kinds of traffic information conditions respectively, which no information, releasing historical information and releasing predictive information. Finally, by setting a simple road network with two parallel paths, the drivers’ day-to-day route choice is simulated. The statistical characteristics of drivers’ behavior are computed. Considering user equilibrium as a yardstick, the effects of three kinds of traffic information are compared. The results show that the impacts of traffic information on drivers are related to the random level of driver’s route choice and reliance on the information. In addition, the road network cannot reach user equilibrium in three kinds of information. This research results can provide a useful reference for the application of traffic information system.

Modeling stochastic perception error in the mean-excess traffic equilibrium model

In this paper, we extend the α-reliable mean-excess traffic equilibrium (METE) model of Chen and Zhou ( Transportation Research Part B 44(4), 2010, 493–513) by explicitly modeling the stochastic perception errors within the travelers’ route choice decision processes. In the METE model, each traveler not only considers a travel time budget for ensuring on-time arrival at a confidence level α, but also accounts for the impact of encountering worse travel times in the (1 − α) quantile of the distribution tail. Furthermore, due to the imperfect knowledge of the travel time variability particularly in congested networks without advanced traveler information systems, the travelers’ route choice decisions are based on the perceived travel time distribution rather than the actual travel time distribution. In order to compute the perceived mean-excess travel time, an approximation method based on moment analysis is developed. It involves using the conditional moment generation function to derive the perceived link travel time, the Cornish–Fisher Asymptotic Expansion to estimate the perceived travel time budget, and the Acerbi and Tasche Approximation to estimate the perceived mean-excess travel time. The proposed stochastic mean-excess traffic equilibrium (SMETE) model is formulated as a variational inequality (VI) problem, and solved by a route-based solution algorithm with the use of the modified alternating direction method. Numerical examples are also provided to illustrate the application of the proposed SMETE model and solution method.

운전자의 일관성 있는 통행시간 인지 행태에 기반한 일별 동적 모형

This study develops a day-to-day dynamics modeling framework, incorporating a consistent drivers` travel time perception behavior and traffic information provision. Descriptive traffic information is updated and provided to the subscribers making a final decision on route choice. Nonsubscribers(not equipped any information devices) are assumed to obtain daily traffic information from their experience or friends or other public agencies. Drivers` route choice behavior is modeled based on boundedly-rational behavior rules. A microscopic traffic simulation model is adopted to evaluate the network system performance. Numerical experiments on a real world network have demonstrated the convergent property of the proposed model and the effectiveness of the consistent perception model.

A fuzzy traffic assignment algorithm based on driver perceived travel time of network links

Traffic assignment is a step of travel demand estimation. Given a trip origin–destination demand matrix, this step determines traffic flow in each link, according to assumptions based on the behavior of drivers. Conventional assignment algorithms, which are mostly based on the Wardrop first principle of user equilibrium, assume that all drivers choose the shortest path to the destination, based on the same travel time computed by travel time functions. However, in reality, driver perception of travel time varies for a specific route. This paper presents a traffic assignment algorithm which assumes that driver perception of travel time affects route choices. Fuzzy set theory is used to define travel time perceived by drivers. A fuzzy equilibrium is suggested for the prediction of network flows. Next, a Fuzzy Incremental Traffic Assignment algorithm (FITA) is developed to utilize route Perceived Travel Time (PTT) for reaching the suggested fuzzy equilibrium. The FITA is used for a real network traffic assignment in Mashhad, which is large city in Iran. Traffic flow that is estimated by a FITA and by conventional algorithms is compared to real observed volumes, which indicate that a FITA is more accurate than conventional traffic flow estimation algorithms.

Agent-based approach to model commuter behaviour's day-to-day dynamics under pre-trip information

This study reports on the tentative use of a multi-agent micro-simulation framework to address the issue of assessing commuter behaviour's day-to-day dynamics under pre-trip information. A Bayesian updating model is adopted to capture the reasoning mechanism by which commuters update their travel time perceptions from one day to the next in light of information and their previous experience. The population of commuters is represented as a community of autonomous agents, and travel demand results from the decision-making deliberation performed by each individual of the population as regards route and departure time. The reasoning mechanism of commuters is modelled by means of a Belief, Desire and Intention architecture, which has been a central theme in the multi-agent systems literature since the early 1990s. Each part of this architecture is specified by a multi-agent programming language named as AgentSpeak (L). A simple simulation scenario was devised using a combination of Jason (a multi-agent simulator) and Paramics (a traffic simulator). The simulation results show that the overall performance of the system is very likely affected by exogenous information and personal travel experiences; also, accurate information can greatly affect driver's switching activities and improve daily commuting conditions. Moreover, the combination of micro-simulation and agent-based modelling technique shows a great potential to represent more realistic and more complex driver's behaviour under intelligent transport system environment.