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With the deployment Automatic Vehicle Location systems to monitor transit vehicles (and other fleet vehicles), there have been several efforts in recent years that seek to leverage these data to also obtain arterial travel times for the private vehicle population. The fleet vehicle data capture the traffic conditions, but they also capture behavior unique to the fleet, e.g., servicing passengers at a bus stop. There are several strategies used in conventional practice to eliminate the biases that occur in the vicinity of the bus stop. While investigating the benefits of using a perception sensor to identify ambient traffic conditions around the bus and correct for transit operations, the present work revealed that even a perfect correction for local conditions resulted in large deviation between the travel time measured from the bus and the actual travel time experienced by the surrounding private vehicles. This research uncovered a fundamental issue affecting almost all systems that use buses as arterial travel time probes: the fact that transit operations inevitably pull the bus out of the traffic signal progression no matter what corrections are made locally at the bus stop. The impact at subsequent traffic signals far downstream of a bus stop can be much larger than the local effects at the bus stop itself. This point is an important finding for any system that seeks to use transit vehicle probes to estimate the private vehicle travel times. To date the literature has made little consideration of the signal progression biases relative to the private vehicles that occur far beyond the bus stop. Finally, though the focus the present work is buses, the basic findings should apply to other fleets used as travel time probes as well if the given fleet behavior differs from the private automobiles. [ABSTRACT FROM AUTHOR]

This paper investigated the impact that the 2013 United States federal government shutdown had on the transportation system in Northern Virginia. Transit ridership data before and during the shutdown were provided by three transit agencies operating in Washington, DC, and its Northern Virginia suburbs. A month of pre-shutdown road volumes and travel times were collected for comparison with the 2-week shutdown period and 2 weeks after the shutdown. As expected, roadway volumes, high occupancy vehicle (HOV) volumes, and transit ridership all saw reductions during the shutdown, with impacts being larger in the second week of the shutdown than the first week. The modes that saw the largest impacts were HOV and the local bus service in the counties surrounding Washington, DC. This is likely unsurprising since these modes are often used by work travelers for their daily commute. Changes were more modest for the Washington Metropolitan Area Transit Authority (WMATA) system, arterials, and freeway general purpose lanes. During the first week, volume changes were often not statistically significant on these facilities. The second week saw similar trends of around a 3 to 5% reduction in daily travel on both the roadway and WMATA systems. Reductions in average travel times as well as improvements in travel time reliability were also observed during the shutdown. However, these benefits were more significant for morning commute trips than evening commute trips as well as for general purpose lanes than HOV facilities. After federal workers went back to work, volumes largely returned to normal. However, some of the additional volume seen at midday during the shutdown was still present soon after the return to work, suggesting that the midday behavior changes took longer to dissipate. The post-PM peak period and weekends after the return to work showed some higher volumes than the pre-shutdown periods, suggesting that some recreation or leisure trips may have been postponed during the shutdown and took place after the return to work. [ABSTRACT FROM AUTHOR]

Public transport travel-time variability (PTTV) is essential for understanding the deteriorations in the reliability of travel time, optimizing transit schedules, and route choices. This paper establishes the key definitions of PTTV which firstly include all buses, and secondly include only a single service from a bus route. The paper then analyzes the day-to-day distribution of public transport travel time by using transit signal priority data. A comprehensive approach, using both parametric bootstrapping Kolmogorov-Smirnov test and Bayesian information criterion technique is developed, recommends lognormal distribution as the best descriptor of bus travel time on urban corridors. The probability density function of lognormal distribution is finally used for calculating probability indicators of PTTV. The findings of this study are useful for both traffic managers and statisticians for planning and analyzing the transit systems. [ABSTRACT FROM AUTHOR]

The use of Bluetooth detection systems for collecting travel time data is becoming increasingly popular because data can be collected continuously at a relatively low cost. However, representativeness of the data can be problematic, given the relatively low detection efficiency of Bluetooth monitoring systems. This paper investigates the use of multiple sensors with individual antennas to the same Bluetooth data collection device to increase the number of identifications from passing vehicles at each data collection point. Increased detection potentially increases the number of travel time measurements, increasing the potential for the measurements to better represent the travel conditions on the roadway. To test this approach, devices with up to 10 Bluetooth sensors were deployed concurrently on a freeway overpass. The marginal benefit of additional Bluetooth sensors in the array was investigated. The additional detections captured by adding up to six Bluetooth sensors in a single sensor array outnumber the detection losses associated with possible radio interference between the Bluetooth sensors or in device-to-sensor communication. On average, additional Bluetooth device media access control (MAC) addresses were detected by increasing the number of sensors from one to two (61% increase), two to three (27% additional increase), three to four (16% additional increase), four to five (11% additional increase), and five to six (8% additional increase). Also, it was observed that additional travel time measurements were collected by adding the number of sensors from one sensor at both locations to a two-sensor setup at both locations (between 110 and 156%), from a two-sensor setup to a three-sensor setup (between 44 and 60%), from a three-sensor setup to a four-sensor setup (between 30 and 47%), from a four-sensor setup to a five-sensor setup (between 10 and 20%), and from a five-sensor setup to a six-sensor setup (between 8 and 17%). This field study indicates that there are potential benefits to using multisensor Bluetooth arrays in lieu of the current practice of using a single sensor configuration to capture additional travel time data from passing vehicles. [ABSTRACT FROM AUTHOR]

This paper presents a Bayesian inference-based dynamic linear model (DLM) with switching based on three-phase traffic flow theory to predict online short-term travel time with plate recognition data. The proposed method combines the DLM model with a Hidden Markov Model (HMM) to capture the probability of flow breakdown and delays associated with congestion. By viewing travel time fluctuations as a time-varying stochastic process due to unforeseen events (e.g., incidents, accidents, or bad weather), the proposed dynamic linear model with Markov switching (SDLM) employs the HMM to determine the optimal traffic state sequence corresponding to a given travel time and flow rate observation sequence. The experimental results based on automatic license plate recognition data of a Jingtong Expressway stretch in Beijing City suggest that the proposed method can provide accurate and reliable travel time prediction under various traffic conditions. [ABSTRACT FROM AUTHOR]

This paper presents a cumulative travel-time responsive (CTR) real-time intersection control algorithm that takes full advantage of connected vehicles (CVs). The potential benefits of the proposed CTR algorithm under varying imperfect CV market penetration rates and traffic congestion conditions were investigated. The core of the CTR algorithm is based on a stochastic state estimation technique utilizing Kalman filtering that is used in estimating the cumulative travel times under imperfect market penetration rates at every update interval. Comprehensive simulation experiments covering varying volume-to-capacity ratios and imperfect market penetration rates were performed at a hypothetical isolated intersection with two through lanes and a single left-turn lane at each approach. At 100% market penetration, the CTR algorithm improved the total delay time and average speed of the intersection by 34 and 36%, respectively, compared to an optimized actuated control. It was found that at least 30% market penetration rates are needed to realize the benefits of the CTR algorithm. [ABSTRACT FROM AUTHOR]

The design of urban bus transit systems aims to determine a network configuration with a set of bus lines and associated frequencies that achieve the targeted objective. This paper presents a methodology framework to formulate and solve the bus transit network design problem (TNDP). It first proposes a TNDP taking into account the travel time stochasticity. A robust optimization model is formulated for the proposed problem, which aims to minimize the sum of the expected value of the operator cost and its variability multiplied by a weighting value. A heuristic solution approach, based on -shortest path algorithm, simulated annealing algorithm, Monte Carlo simulation, and probit-type discrete choice model, is subsequently developed to solve the robust optimization model. Finally, the proposed methodology is applied to a numerical example. [ABSTRACT FROM AUTHOR]

From the historical bus trajectories, it was found that the headway bias amplified as buses travel on the route. When controls on buses are unavailable, the following buses will maintain the movement tendency toward their previous one, whether being closer to, farther away, or stable, judged by the running state in which buses fall. A tendency-based model for link travel time estimation was proposed, and three tendency-based corrections were introduced in the model, which are the long-term tendency, the short-term tendency, and the combined-term tendency. Then, contrast experiments were conducted in which the boundary of the running state is a control variable to show the performance of the tendency-based model under different boundary values. The experiment results show that, with the increase of the boundary value, the degree of improvement of the tendency-based mode to the historical data-based model first increases, then decreases, and converges to zero finally. The optimal boundary value for the tendency-based model was calibrated, judged by the net number of trips improved and net mean absolute error reduced, and the results show that the long-term and the combined-term tendency-based models have a lower optimal boundary and higher optimization potential, and are faster to be steady enough, which made the short-term tendency-based model less competitive. [ABSTRACT FROM AUTHOR]

Travel speed ties directly to travel time, so it is an important measure for quantifying arterial performance. However, accurately estimating link travel speed for urban arterials is difficult because of traffic fluctuations and stop-and-go conditions caused by signal control. This research proposes a two-step empirical approach to effectively estimate the link-journey speeds using only advance loop detector outputs. The first step is to estimate the spot speed on the basis of advance loop measurements using Athol's algorithm. The robust regression technique can be used to calibrate the speed estimation parameter (or -factor) in Athol's algorithm. The second step is to use the proposed simplified speed estimation model to estimate the link speed using only the calculated loop-spot speed without any knowledge of signal timing plans. Traffic operations in the central business district of the City of Bellevue, Washington, are simulated in the VISSIM traffic simulation model. The test results show that only 50 cycles of data are needed to calibrate the -factor in loop-speed estimation and the same datasets can be used to calibrate the proposed link-speed model. Using this model, the average mean absolute error over the study links is reduced from 4.24 to 1.51 mph. With proper calibration, this average error can be further reduced to 0.91 mph. The results are encouraging and satisfactory. The results also show that the accuracy of speed estimation may be further increased when more data are applied for calibration. [ABSTRACT FROM AUTHOR]

Travelers' perception of reliability of a road network is typically based on factors contributing to both recurring and nonrecurring congestion. However, literature documents little research to integrate, estimate and assess congestion using both these disparate congestion components. Integrating recurring and nonrecurring congestion components to estimate congestion helps address questions such as 'How reliable are roads in the transportation network?' 'Which path is more reliable to reach the destination from an origin within an on-time window during a certain time of the day?' The focus of this paper is to develop and illustrate the working of a methodology to estimate travel time and its variations, travel delay index due to crashes and their severity, congestion score and reliability of each link in the network. Traffic volume, link capacity, travel speed, crashes and their severity, and estimated time taken for normal traffic conditions to restore after a crash are used in the computations. Temporal variations in travel time and crashes for each link are also studied as travel demand and crash occurrence depend on the time of the day. Sensitivity analysis is also conducted to examine and assess reliability of links based on variations in weights to integrate recurring and nonrecurring congestion components. Data collected for the city of Charlotte in Mecklenburg County, North Carolina are used to demonstrate the methodology. [ABSTRACT FROM AUTHOR]

Transit service reliability is an important determinant of service quality, which has been mainly studied from the perspective of passengers waiting at stops. Day-to-day variability of travel time also deteriorates service reliability, but is not a well-researched area in the literature partly due to the lack of comprehensive data sets on bus travel times. While this problem is now being addressed through the uptake of global positioning system (GPS)-based tracking systems, methodologies to analyze these data sets are limited. This paper addresses this issue by investigating day-to-day variability in public transport travel time using a GPS data set for a bus route in Melbourne, Australia. It explores the nature and shape of travel time distributions for different departure time windows at different times of the day. Factors causing travel time variability of public transport are also explored using a linear regression analysis. The results show that in narrower departure time windows, travel time distributions are best characterized by normal distributions. For wider departure time windows, peak-hour travel times follow normal distributions, while off-peak travel times follow lognormal distributions. The factors contributing to the variability of travel times are found to be land use, route length, number of traffic signals, number of bus stops, and departure delay relative to the scheduled departure time. Travel time variability is higher in the AM peak and lower in the off-peak. The impact of rainfall on travel time variability is only found significant in the AM peak. While the paper presents new methods for analyzing GPS-based data, there is much scope for expanding knowledge through wider applications to new data sets and using a wider range of explanatory variables. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm for an upgrading arc median shortest path problem for a transportation network. The problem is to identify a set of nondominated paths that minimizes both upgrading cost and overall travel time of the entire network. These two objectives are realistic for transportation network problems, but of a conflicting and noncompensatory nature. In addition, unlike upgrading cost which is the sum of the arc costs on the path, overall travel time of the entire network cannot be expressed as a sum of arc travel times on the path. The proposed solution approach to the problem is based on heuristic labeling and exhaustive search techniques, in criteria space and solution space, respectively. The first approach labels each node in terms of upgrading cost, and deletes cyclic and infeasible paths in criteria space. The latter calculates the overall travel time of the entire network for each feasible path, deletes dominated paths on the basis of the objective vector and identifies a set of Pareto optimal paths in the solution space. The computational study, using two small-scale transportation networks, has demonstrated that the algorithm proposed herein is able to efficiently identify a set of nondominated median shortest paths, based on two conflicting and noncompensatory objectives. [ABSTRACT FROM AUTHOR]

In the field of advanced traveler information systems, travel time reliability contributes significantly to the utility of traffic information affecting the traveler’s choice. The exact estimation of the variance in travel times is fundamental to calculating reliability indices. A method for predicting the dynamic variance in estimated link travel times is described. The dynamic variance is allowed to vary dependent on variances for previous time periods, which is typically ignored in conventional time-series analysis. We adopt the autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model in which the ARMA model and the GARCH model are combined. In parallel, the generalized Pareto distribution (GPD) is employed in the computation of percentile to overcome the asymmetry in travel time distribution. The autocorrelation of dynamic variance is identified in links located in urban congested areas. The use of the ARMA-GARCH model yielded statistically significant outcomes in estimating dynamic variances in travel times. In particular, for a link with higher level of congestion, the ARMA-GARCH model along with GPD has been proven to be more promising. [ABSTRACT FROM AUTHOR]

Travel time is an important parameter in evaluating the operating efficiency of traffic networks, in assessing the performance of traffic management strategies, and as input to many intelligent transportation systems applications such as advanced traveler information systems. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as inductance loop detectors. Because of the widespread deployment of loop detectors, they are one of the most widely used inputs to travel time estimation techniques. There are different methods available to calculate the travel time from loop detector data, such as extrapolation of the point speed values, statistical methods, and models based on traffic flow theory. However, most of these methods fail during the transition period between the normal and congested flow conditions. The present study proposes several modifications to an existing traffic flow theory based model for travel time estimation on freeways, such that the model can estimate travel time for varying traffic flow conditions, including transition period, directly from the loop detector data. Field data collected from the I-35 freeway in San Antonio, Tex., USA, are used for illustrating the results. Automatic vehicle identification data collected from the same location are used for validating the results. Simulated data using CORSIM simulation software are also used for the validation of the model. [ABSTRACT FROM AUTHOR]

Improved highway networks result in net benefits to society, of which the reduction in travel time is one of the most important. Similarly, the delay in travel due to construction and maintenance operations represents a real cost to users. Assignment of the value of time in the cases of both benefit and cost involves judgment, as individuals value time differently and adjust for travel time changes in various ways. Values based on wage rates traditionally have been used because they are convenient, but an alternative approach based on purchase of leisure time is proposed. A full life-cycle cost assessment of a project requires an evaluation of future savings in travel time projected over a project’s lifetime, as well as delay costs associated with construction and subsequent maintenance operations. Due to the economic, as well as social complexity of user travel time, alternative measures of valuation and its network effects need to be considered. [ABSTRACT FROM AUTHOR]

This study analyzed the operational effects of a widely used access management treatment: using U-turns as alternatives to direct left-turns from driveways. Data were collected at 34 selected roadway segments in central Florida. Delay and travel time were compared for various driveway left-turn alternatives under different levels of driveway volume and major road through traffic volume. The left-turn alternatives considered include direct left-turns (DLT), right-turns followed by U-turns (RTUT) at median openings, and right-turns followed by U-turns at signalized intersections. A binary logit model was developed to estimate how many drivers would like to make a RTUT instead of a DLT under different traffic and roadway geometric conditions. It was found that vehicles making RTUT at a downstream median opening before a signalized intersection have comparable total travel time as compared with those making DLT at a driveway; and the percentage of drivers selecting RTUT increases with the upstream through traffic volume, left-turn volume from major road into the driveway, and the total left-turn traffic demand at a driveway. [ABSTRACT FROM AUTHOR]

This paper presents estimations of willingness-to-pay (WTP) values in the context of rural bus service in a developing country. Using stated choice data collected from rural bus users, WTP values are estimated by developing standard multinomial logit model (MNL) and three different random parameter logit (RPL) models: One with independent or uncorrelated responses, another taking into account the correlations among responses, and the other allowing heterogeneity around the mean of in-vehicle travel time. While developing the RPL models, successful application of the sparsely used constrained triangular distribution is demonstrated. WTP values are estimated separately for commuting and noncommuting trips. Higher WTP values for travel time are observed for commuting trips, while for noncommuting trips higher WTP values are found for qualitative attribute. Preference heterogeneity associated with the mean is investigated, and the “travel distance” is found to have a statistically significant decomposition effect on the mean of in-vehicle travel time for commuting trips. The traditional MNL model, in general, is found to underestimate the WTP values as compared to RPL models. [ABSTRACT FROM AUTHOR]

It is widely acknowledged that traffic information has the potential of increasing the reliability in road networks and in alleviating congestion and its negative environmental and societal side effects. However, for these beneficial collective effects to occur, reliable and accurate traffic information is a prerequisite. Building on previous research, this article presents a reliable framework for online travel time prediction for freeways, which could, for example, be used to generate traffic information messages on so-called dynamic route information panels on freeways. Central in this framework is a so-called state-space neural network (SSNN) model, which learns to predict travel times directly from data obtained from real time traffic data collection systems. In this article we show that by using an ensemble of SSNN models also a measure for the reliability of each prediction can be produced. This enables traffic managers to monitor in real time the reliability of this system without actually measuring travel times. [ABSTRACT FROM AUTHOR]

In recent years automatic number plate recognition systems have been increasingly used to monitor urban link travel time. However, the set of travel times produced will contain erroneous records for a variety of reasons including the misreading of the licence plates by the camera, vehicles stopping en-route, and vehicles taking nondirect routes between the two camera sites. This paper proposes a novel technique called the overtaking rule, which uses the serial temporal structure of the data to filter out these erroneous travel time records. The paper proposes a methodology to measure the performance of a licence plate cleaning method, and it is subsequently shown that the overtaking rule outperforms existing cleaning methods. [ABSTRACT FROM AUTHOR]

Travel time estimation models, which rely on speed data provided from point detectors (usually inductive loops), have application in travel time prediction and network performance monitoring. Unfortunately there are limited, and at times counterintuitive, results in the literature about their performance. This paper focuses on the field evaluation of four speed-based travel time estimation models, namely, the instantaneous model, the time slice model, the dynamic time slice model, and the linear model. Those models are evaluated using data from two operational motorways in Melbourne, Australia. Travel time estimation errors are quantified against actual travel times measured using a timed number plate survey and time-stamped toll tag data. There was little difference in the travel time estimation error across the models and they were all found to underestimate actual travel times. Errors ranged from about 7% in the off peak up to 15% in the peak. Marginal improvements in model performance were achieved through careful selection of which detectors provide input for each section (upstream, downstream, or the average of those values) and by conversion of the inputs from time mean speed to an estimate of space mean speed. [ABSTRACT FROM AUTHOR]

An efficient ramp control strategy may not be politically attractive due to its negative impacts on equity. This paper addresses this issue and presents a new objective for ramp metering—minimizing weighted travel time—which is able to balance efficiency and equity. A quasioptimization/simulation approach is used to achieve the new objective. BEEX, a new family of control strategies with various degrees of equity consideration, are developed and used to demonstrate this approach. [ABSTRACT FROM AUTHOR]

Transportation Analysis and Simulation System (TRANSIMS) is a microsimulation model, which uses census data to generate a synthetic population and assigns activities using activity survey data to each person of every household of the synthetic population. The synthetic households generated from the census data are matched with the survey households based on their demographic characteristics. The activities of the survey household individuals are then assigned to the individuals of the matched synthetic households. A classification and regression tree algorithm program is used to build the matching tree for the activity survey households based on some demographic independent variables. The current TRANSIMS model utilizes times spent at activities as dependent variables for building the classification tree. This research instead uses the travel times for travel between activities as dependent variables, i.e., using the travel time pattern instead of activity time pattern to match the persons in the survey households with the synthetic households. This approach assumes that if the travel time patterns are the same then we can match the survey households to the synthetic population, i.e., people with similar demographic characteristics tend to have similar travel time patterns. The algorithm of the activity generator module along with the original set of dependent variables were first used to generate a base case scenario. Further tests were carried out using an alternative set of dependent variables in the algorithm. A sensitivity analysis was also carried out to test the affect of different sets of dependent and independent variables in generating activities using the algorithm of the activity generator. The paper documents the results from all these tests and provides conclusions and recommendations. [ABSTRACT FROM AUTHOR]

Transit travel time and operating speed influence service attractiveness, operating cost, and system efficiency. The objective of this paper is to estimate the values of parameters that affect the total travel time for a particular bus route in Portland, Oregon. The Metropolitan Transportation District of Oregon (TriMet) provides transit service in the three-county Portland metropolitan area. TriMet has implemented a Bus Dispatch System (BDS) as a part of its overall service control and management system. This BDS provides a rich array of archived data that were used in this study to develop the trip time model for the route under study. This trip time model provides heightened understanding of the factors that affect the trip time on the route. The value of the model was revealed when sensitivity analyses were performed using data from the studied route. This analysis concluded that improvements can be readily achieved after understanding the factors that affect total trip time. Finally, some conclusions and recommendations are presented and suggestions for additional research are provided. [ABSTRACT FROM AUTHOR]