Moments Analysis for Improving Decision Reliability Based on Travel Time

Travel time and trajectory moments of conservative solutes in two-dimensional convergent flows

The State Railway of Thailand provides passengers with train location information on their Web site, which includes the name of the last station that each train arrives at or departs from, along with the timestamps and the accumulative train delay (in minutes) from the train timetable. This information allows passengers to intuitively predict the arrival time at their station by adding the last known train delay to the scheduled arrival time. This paper aims at providing a more accurate prediction of passenger train's arrival times using the historical travel times between train stations. Two algorithms that use train location information and historical travel times are proposed and evaluated. The first algorithm uses the moving average of historical travel times. The second algorithm utilizes the travel times of the k-nearest neighbors (k-NN) of the last known arrival time. To evaluate the proposed algorithms, we collected six months of data for three different trains and calculated prediction errors using mean absolute error (MAE). The prediction errors of the proposed algorithms are compared to the prediction errors of the baseline algorithm that predicts the arrival time by adding the last known train delay to the scheduled train arrival time. Both algorithms outperform the baseline prediction. The algorithm based on moving average travel time improves the prediction error by 22.9 percent on average, and the algorithm based on k-NN improves the prediction error by 23.0 percent on average (k=16).

Modeling of the Market Penetration of Advanced Traveler Information Systems Using a Mixed Network Equilibrium Model

Emissions Impact of Road Traffic Incidents Using Advanced Traveller Information Systems in a Regional Scale

This paper is concerned with the task of dynamic router real-time travel time prediction for an arbitrary origin-destination pair on a map. The predicting travel time is based on the historical travel time and the current travel time. The historical travel time is calculated by speeds. The traffic pattern similar to the current traffic are searched among the historical patterns and closest matched patterns are used to extrapolate the present traffic condition. The method is combined the historical traffic patterns with real-time traffic data as a linear.A router is chosen from a few candidate routers based on the prediction technique. The resulting model is tested with realistic traffic data, and is found to perform well.

Evaluating the impact of public transport travel time inaccuracy and variability on socio-spatial inequalities in accessibility

An optimal global algorithm for route guidance in advanced traveler information systems

Variation in Patients' Travel Times among Imaging Examination Types at a Large Academic Health System

Suppose that in an urban transportation network there is a specific advanced traveler information system (ATIS) which acts for reducing the drivers' travel time uncertainty through provision of pre‐trip route information. Because of the imperfect information provided, some travelers are not in compliance with the ATIS advice although equipped with the device. We thus divide all travelers into three groups, one group unequipped with ATIS, another group equipped and in compliance with ATIS advice and the third group equipped but without compliance with the advice. Each traveler makes route choice in a logit‐based manner and a stochastic user equilibrium with multiple user classes is reached for every day. In this paper, we propose a model to investigate the evolutions of daily path travel time, daily ATIS compliance rate and yearly ATIS adoption, in which the equilibrium for every day's route choice is kept. The stability of the evolution model is initially analyzed. Numerical results obtained from a test network are presented for demonstrating the model's ability in depicting the day‐to‐day and year‐to‐year evolutions.

Exploring travel time distribution and variability patterns is essential for reliable route choices and sophisticated traffic management and control. State-of-the-art studies tend to treat different types of roads equally, which fails to provide more detailed analysis of travel time characteristics for each specific road type. In this study, based on a vast amount of probe vehicle data, 200 links inside the Third Ring Road of Beijing, China, were investigated. Four types of roads were covered including urban expressways, auxiliary roads of urban expressways, major roads, and secondary roads. The day-of-week distributions of unit distance travel time were first analyzed. Kolmogorov-Smirnov test, Anderson-Darling test, and chi-squared test were employed to test the goodness-of-fit of different distributions and the results showed lognormal distribution was best-fitted for different time periods and road types compared with normal, gamma, and Weibull distribution. In addition, four reliability measures, that is, unit distance travel time, coefficient of variation, buffer time index, and punctuality rate, were used to explore the day-of-week travel time variability patterns. The results indicated that urban expressways, auxiliary roads of urban expressways, and major roads have regular and distinct morning and afternoon peaks on weekdays. It is noteworthy that in daytime the travel times on auxiliary roads of urban expressways and major roads share similar variability patterns and appear relatively stable and reliable, while urban expressways have most reliable travel times at night. The results of analysis help enable a better understanding of the volatile travel time characteristics of each road type in urban network.

The paper formulates risk taking behavior over general queing network with advanced traveler information systems (ATIS). It supposes that drivers equipped with ATIS will make their choices based on the complete information about travel time and its possible variations, while drivers without ATIS have to choose based on their past experience and subjective understandings. The mixed behavior time dependent equilibrium problem is formulated as a nonlinear complementarity problem and a heuristic route/time swapping approach is proposed to solve the problem. Also investigated is the implementation of ATIS on a toy network under varied values of market penetration.

Travel time reliability is a major factor affecting travelers’ route and mode choice behaviors, which is an important aspect of intelligent transportation systems. The calculation of travel time reliability measures is founded on the travel time distribution function. In practice, directly fitting the route-level travel time distribution function using complete route-level travel time data is challenging. As an alternative to route-level travel time data, segment-level travel time data are more abundant and easier to obtain. However, existing estimation methods of route travel time distribution function either neglect travel time correlation among segments, or rely on historical route-level data, or assume a single distribution type for all segments or routes. To address these issues, this paper proposes a moment-based method of estimating the route travel time distribution function that uses only segment-level travel time data. The proposed method estimates the correlation coefficients of segment travel times using the Rearrangement Inequality. Then, the four moments of route travel time are analytically derived. By further employing the corrected Cornish-Fisher expansion, the proposed method ultimately provides an analytical expression for the route travel time distribution function without assuming the distribution functions of segments or routes. The accuracy of the method is examined using data from a license plate recognition system on an expressway in Shenzhen and taxis GPS in Beijing. The proposed method enables a precise calculation of route travel time reliability in road networks that is quite important to travelers’ behavioral decisions and managers’ operational decisions.

Capturing Value of Reliability through Road Pricing in Congested Traffic under Uncertainty

Despite the importance of reliability, few countries monitor it or explicitly incorporate it into transport policy making. Nevertheless, a number of policy options are available to improve this aspect of transport management. Active management of the network through ramp metering is recognized as an efficient way to control motorway traffic and improve average travel time. Far less is said about the reliability benefits of ramp metering. This paper assesses reliability benefits of ramp metering on the basis of a before-and-after study on the A6W, a French motorway near Paris. Several indicators for travel time variability are applied before and after the introduction of ramp metering. For purposes of taking reliability into account in policy impact evaluation, cost–benefit assessment provides a consistent framework within which to assess the monetized benefits. The monetary value of the reliability benefits of ramp metering is therefore calculated, and the policy implications of the results are discussed. The results suggest that in addition to providing gains in average travel time, ramp metering significantly improves reliability of travel times. It is also proposed that indices such as buffer time or planning time are useful for communicating the results, both for network operators and for users. Failing to unbundle the time-savings benefits of a project between average travel time and the variability in travel time is likely to lead to suboptimal policy solutions. Managing existing capacity better can be a cost-effective way to improve both average travel time and the variability in travel time.

A methodology for evaluating the unconditional and conditional moments of travel time for a sorbing solute is presented. The approach is applicable for any flow configuration and for a wide range of mass transfer rate‐limited linear processes. The methodology is applicable to the general case of spatially variable hydrological and chemical parameters. The sorption model used to derive the temporal moments is that of a continuous distribution of mass rate coefficients [Haggerty and Gorelick, 1998]. Models such as instantaneous equilibrium, first‐order and two‐site sorption kinetics, among others, can be considered as particular cases of this general model. Using a deterministic approach, the low‐order moments of the breakthrough curves for reactive solutes can be obtained as a function of those for conservative tracers. Using a stochastic approach, the unconditional low‐order statistics of the travel time moments can be obtained. These moments depend on the statistics of two Lagrangian functions, the travel time for a conservative solute, and an integral of the variations of the chemical parameters weighted by the inverse local velocity along the trajectory. Finally, conditional temporal moments are derived. Moments can be conditioned to any type of information, hard or soft, hydraulic or geochemical. Conditioning is found to reduce uncertainty, characterized by a reduction in the variance of the travel time. The general results are particularized for both uniform in the mean and convergent flow conditions and for simple sorption models such as linear instantaneous equilibrium and first‐order kinetics. In all such cases, close‐form results, based on small perturbations expansions, are presented for the travel time moments.