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Queue length is one of the indicators of the state of traffic and is often used to measure the operational state of signalized intersections. Many studies have proposed estimating queue length from vehicle trajectory data (e.g., floating car GPS data); however, its sparse spatio-temporal distribution and low sampling frequency present substantial challenges in practice. In some jurisdictions, the widespread deployment of automatic vehicle identification (AVI) technologies presents the opportunity to improve queue length estimation at signalized intersections by combining AVI and trajectory data from floating (probe) vehicles. The method proposed in this paper is applicable for both under and oversaturated traffic conditions, is evaluated using field data [Next Generation Simulation (NGSIM) data set] and simulation data, and is compared to ground truth and the method proposed by the author Tan. The results from the field data evaluation indicate that the method provides a good estimation of the queue size (mean average error less than three vehicles for a floating vehicle penetration rate of 5% and a GPS sampling interval of 10 s). The simulation data evaluation indicated that the proposed method performs better than the Tan's method. [ABSTRACT FROM AUTHOR]

Performance measures that are commonly used in winter maintenance provide valuable information. However, some metrics are subjective (i.e., visual inspection), rely on historical records, depend on agencies' practices, or only capture specific information. Winter maintenance performance measures are evaluated independently, and relationships among variables are not assessed. Therefore, the metric travel disruption (TD) is proposed in this study to perform a multispectral evaluation with input, output, and outcome-based performance measures to compare salt brine with solid salt applications. The metric TD integrates different sources of information to estimate a continuous performance measure that accounts for storm, vehicle operations, and roadway conditions over time instead of intermittent measurements or one-time observations, such as roadway friction or time to bare/wet. The input performance measures included storm conditions, type of chemical material, frequency of application, and application rate. The output performance measures were the amount of material used and the lane kilometer (miles) covered. The outcome performance measures consisted of time to bare/wet, speed reduction period, speed recovery period, storm impact period, maximum speed reduction, and TD. For salt brine, previous research has shown reduced salt usage, shorter times to bare/wet, and higher friction. However, there are some concerns regarding the operational and safety performance of roadways that are treated with salt brine. Winter treatment field data and vehicle probe data from the National Performance Management Research Data Set (NPMRDS) were collected from the study (salt brine) and control (solid salt) routes in Wisconsin. For the amount of salt used, the study routes used 32.6% less overall salt than the control routes per storm. The TD indicated that, on average, there was no statistically significant difference between the study and control routes. Despite the lower amount of salt that was used, roadways that were treated with salt brine had a similar operational performance as roadways that were conventionally treated with solid salt, which made salt brine application a cost-effective strategy. This evaluation demonstrated a multispectral performance evaluation of winter maintenance to appropriately quantify the effectiveness of treatments. [ABSTRACT FROM AUTHOR]

Estimation of passenger car unit (PCU) values is very important for traffic capacity analysis and other relevant applications such as determining the saturation flow rate and signal design. However, the usual methods are not completely analogous for mixed traffic due to the presence of a wide variety of vehicle types, poor lane discipline, variability of intraclass vehicles. The parameters required for PCU estimation under mixed traffic are relatively more complex to measure, causing a severe data limitation. The accurate estimation of PCUs relies on the collection of sufficient amounts of data, and unmanned aerial vehicles (UAV) like drones are used in recent years by researchers as they significantly reduce efforts of data collection and extraction. This paper illustrates three methods for PCU estimation under mixed traffic on two signalized intersections of China using UAV monitoring data. The first method is based on the travel time of a vehicle while clearing the intersection. The second method is based on the average speed estimated in terms of different vehicle categories. The lagging headway ratio is used as the basis for PCU estimation in the third method. The author explores the effect of the driving direction and compared the PCU values at different time periods. The results show that the PCU value of vehicles turning left is always greater than that of going straight. The usage of a combined parameter is more appropriate than a single parameter in mixed traffic. The values are similar to those of India but are quite different from the United States. The values obtained in this study can be used as a guideline in the traffic analysis at signalized intersections in China and generally in other developing countries [ABSTRACT FROM AUTHOR]

Travel time to a destination is a key piece of information that motorists desire. Traditionally, transportation agencies collect traffic state information with costly measurement sensors. In the last decade, private sector data, as an emerging data source, has been gradually adopted by many public agencies for performance measurement and travel time provision, mostly on freeways. Agencies have also used data from infrastructure sensors and floating cars to validate the quality of the private sector data on both freeways and arterials under different conditions. Although freeway data quality has been validated by many studies, nonfreeway travel time data have been considered unreliable, particularly when the segment is congested and high-density signals exist. In recent years, data vendors started to provide high-resolution travel time data, which are provided on much shorter segments than conventional traffic message channels (TMC). This study investigates the data quality of this new data source for providing travel times on nonfreeways. It evaluates the selected private sector data on nonfreeways by using floating car and Wi-Fi travel time data as the ground truth. The results show that the new private sector data on nonfreeways are generally acceptable for segments with low and moderate congestion. With higher congestion, the data quality is site-dependent. A neural network model is proposed to implicitly identify the error of a site and provide enhanced travel times. It is recommended that these new data should be validated for the deployment sites due to the site-dependent errors, and advanced data modeling techniques can be used to compensate for such errors. [ABSTRACT FROM AUTHOR]

Manholes in combined sewers may become surcharged during storm events, resulting in complex mixing conditions. Although manhole hydrodynamics are reasonably well understood, predicting mixing across a surcharged manhole remains a challenge. An analytical compartmental mixing model for manholes, based on jet theory, has been further developed and applied to generate cumulative residence time distributions (CRTDs), which describe mixing. The modeled CRTDs were compared with the experimentally derived CRTDs of over 850 manhole configurations to evaluate how well the new compartmental model represents physical processes. The model underpredicts short-circuiting in manholes with manhole diameter to pipe diameter ratios greater than 4.4 and consequently overestimates mixing. Otherwise, the modeled CRTDs show good agreement with the experimental CRTDs. The new compartmental model represents key manhole hydrodynamics that are not represented in current software modeling packages, which assume manholes are instantaneously well-mixed. The compartmental model provides good predictions of the experimental downstream concentration profiles, although with reduced peak concentrations in those manhole configurations where short-circuiting is not well-predicted. Despite this, the compartmental model still predicts concentrations downstream of a manhole in closer agreement with the recorded data than the complete instantaneously well-mixed assumption. As an analytical model requiring no inputs other than manhole geometry, the new compartmental model applies to a wide range of manhole configurations, is robust, and is useful for predicting manhole mixing in practical applications. [ABSTRACT FROM AUTHOR]

An analytical solution is proposed for a two-dimensional, fully-penetrating ditch drainage system by considering the boundaries at the ditch face and at the soil surface to change with time. The general assumption of instantaneous boundary impositions at the ditch face and at the surface of the soil is replaced by continuous time-varying boundary impositions at these faces, as they are expected to be more realistic and readily realized in actual field situations, as compared to static and instantaneous boundaries at the ditches and at the surface of the soil. However, the proposed analytical model is a versatile one, capable of tackling both instantaneous and continuous boundary impositions alike. To ascertain the validity of the solution, a few experiments on them have also been carried out. Further, the correctness of the proposed model is also checked for a simplified flow scenario by comparing it with an existing analytical solution to the problem. The study highlights that with the imposition of time-dependent boundaries, the maximum top discharge value gets reduced by multiple folds as compared to ponded drainage situations in which boundaries have been instantaneously imposed. It is also observed that the time taken by a ponded drainage system to reach a steady-state has a direct correlation with the time required to create steady water depths at the different boundaries of the problem. Further, the pathline and travel time of water particles are both found to be sensitive to the nature and distribution of the time-dependent boundaries of the problem. [ABSTRACT FROM AUTHOR]

Work zones have become the second largest contributor to the nonrecurring delay disruptions of traffic on US highways. The development of a sound model to minimize the total cost (i.e., road user cost and agency cost) by implementing effective traffic management strategies (i.e., traffic diversion or shoulder use) is desirable. The objective of this study was to optimize work-zone schedules and associated characteristics (e.g., maintenance crew, work-zone lengths, and diversion rates) that yield the minimum total cost. Floating car data enable traffic engineers and planners to obtain accurate and reliable traffic measures, such as speed, travel time, and delay. By considering prevailing road and time-varying traffic conditions, the proposed model evaluated the efficiency and effectiveness of traffic management strategies for freeway work zones. A case study was conducted in which the developed model was applied to optimize the work schedule for a road maintenance project on Interstate 80 (I-80) in New Jersey, in which the relationships among the decision variables and model parameters were explored. The findings of this study can assist traffic management decision making to mitigate congestion caused by freeway work zones. [ABSTRACT FROM AUTHOR]

This study utilizes travel time data from a well-equipped Intelligent Transportation System corridor to integrate travel time and traffic signal data with the goal of developing a volume-delay relationship between a signal's volume-capacity ratio and travel time. Specifically, the traffic signal timings were retrieved from an advanced transportation management system, individual travel time data were extracted from a WiFi-based travel time measurement system, and traffic volumes and queues were obtained by observing closedcircuit television video recordings. The collected data were then integrated on a cycle-by-cycle basis by using a C#-automated data postprocessing interface. Several traditional volume-delay functions were calibrated to fit the field data. Also, a new volume-delay function, named So-Stevanovic Volume-Delay Function, was developed to account for exponential behavior of travel time near and beyond the traffic saturation point. The So-Stevanovic Volume-Delay Function satisfied the seven Spiess's requirements for a well-behaved congestion function and produced slightly better results than the best traditional volume-delay function. All of the functions were then tested again on a different road segment in order to validate the results. These tests confirmed the previous findings that the So-Stevanovic Volume-Delay Function was the best predictor of relationship between volume-capacity ratio and link travel time. Further research should be conducted to validate this function in a variety of field traffic conditions. [ABSTRACT FROM AUTHOR]

The objective of this research is to reduce scale effects on surface runoff routing processes in coarse-resolution hydrologic models to produce event hydrograph characteristics (peak and timing) similar to those obtained from fine-resolution models. Herein, this concept is referred to as upscaling. The developed upscaling approach combines statistical and physics-based techniques and is applied to the Ohio River Basin using a synthetic 2-year, 24-h runoff experiment. Cumulative distribution functions (CDFs) for surface flow path travel times based on 90-m-resolution digital elevation model (DEM) data and conceptualized model units representing individual catchments in a model are matched by adjusting surface roughness along simulated hillslopes. The travel time CDF for individual catchments based on 90-m DEM data are approximated using the beta distribution to facilitate applications in large watersheds. Nine model resolutions are considered: 1 (reference model resolution), 3.2, 10, 32, 100, 320, 1,000, 3,200, and 10;000 km2, where model resolution corresponds to the threshold area used to define the underlying river network and catchment boundaries. Simulated hydrographs at the outlet for the eight coarser model resolutions have peak discharges and times to peak that are similar to those obtained from the reference model resolution. To match hydrograph characteristics, surface roughness along the hillslope flow paths are adjusted by, on average, -85 to þ94%, where the positive values are for the 3.2-km2 resolution and the largest reductions are for the 10,000-km2 resolution. [ABSTRACT FROM AUTHOR]

Multimachine scheduling problems with earliness/tardiness costs and sequence-dependent setup times are analogous to the simultaneous irrigation scheduling problem with water travel times between outlets in a canal irrigation system where all the farmers are supplied with identical discharges at their requested time, i.e., arranged demand irrigation scheduling. The multimachine scheduling problem with earliness/tardiness costs even without setup consideration is computationally very demanding and optimum solutions are not possible in practical time limits. The addition of the sequence-dependent setup time and the dual goal of minimizing earliness/tardiness and the number of machines makes it even more difficult, complicated, and novel. For practical applications, meta-heuristics such as genetic algorithms, simulated annealing, or tabu search methods need to be used. This study employs the genetic algorithm (GA) model. The model presented here is an improvement over earlier work as it considers travel time in a multimachine or simultaneous irrigation system and resolves the issue of computational time by using an approximate algorithm instead of an exact algorithm. However, no quantitative comparison can be done with earlier models as the current model accommodates travel time; hence, its objective function is numerically different than earlier models. The problem is successfully modeled using GA and its implementation is demonstrated. No comprehensive data set is available that completes the requirements of rigorous testing of the GA model. Therefore, to evaluate the performance of the GA model with travel time, instances were randomly generated from a uniform distribution, for three different values of travel times. The GA model was able to obtain feasible schedules for all the instances tested. [ABSTRACT FROM AUTHOR]

Derivation of spatial distribution of travel time is required in some conceptual rainfall-runoff transformation models. Application of a kinematic wave (KW) solution to delineate isochrones of travel time may be straightforward, particularly in an overland flow regime. In this article, a new analytical approach based on the diffusive wave (DW) solution is developed for calculation of travel time. The discharge hydrograph over a rectangular plane will be calculated based on the developed analytical DWmethod through the application of the time-area method. Differences between calculated maximum travel time (i.e., equilibrium time), isochrone locations, and outflow discharge between the KWand DW solutions are compared with those of the numerical dynamic wave (DYW) solution. It is shown that analytical DW results are closer to those of the DYW in comparison with those of the KW. The effect of the K0F20 number on the solution accuracy is investigated, where K0 is the kinematic wave number and F0 is the Froude number. It is perceived that as K0F20 decreases, the differences between KWand analytical DWsolutions grow. For instance, at equilibrium time, the new DWsolution improved the results by 5 to 12% over the KW solution. In addition, the DWisochrones lie upstream of the corresponding KWisochrones. Finally, the 1.5 < K0F20 < 20 interval is proposed for using an analytical DW solution in lieu of the KWand DYW solution. [ABSTRACT FROM AUTHOR]

Explicit formulas for the times of arrival of the leading and trailing edges of a contaminant cloud are developed from a one-dimensional advection-dispersion model of transport. These times--defined as the times at which the concentration is some fraction α of the peak concentration--can be determined easily with iterations using a spreadsheet or other software, but this approach becomes time-consuming if many calculations are needed. Also, solutions to the transient storage zone model and its relatives can be made more efficient by identifying the arrival times of the edges of a contaminant cloud. The expressions for the arrival times depend only the fraction α and the Péclet number P. For 0.01 < α < 0.1, the formula for the time of arrival of the leading edge is within 7% for P > 1 and 1% for P > 20, and the expression for the time of arrival of the trailing edge is within 10-20% for P = 1 and less than 1% for P > 6. Despite the criticisms of the advection-dispersion model for transport in real waterways, the formulas based on it predict the arrival times at least as well as previously proposed formulas, as long as the mean velocity over the river reach can be estimated. [ABSTRACT FROM AUTHOR]

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]

Bus travel-time prediction has drawn a lot of research interests in previous literature. This paper proposes a prediction model for bus travel time based on the support vector machine (SVM) regression method. A forgetting factor is introduced to assign the weight to the recent data resulting from the bus running time-based variable quantities. The Grubbs' test method is applied to remove outliers from the input data. The proposed model is assessed with the data of transit route number 23 in the city of Dalian, China. Results show that the SVM with the forgetting factor and the Grubbs' test method is a powerful tool for bus travel-time prediction. [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]

The transportation sector is the second largest source of greenhouse gas (GHG) emissions in the United State. Developing underutilized urban industrial sites with certain characteristics (i.e., close proximity to transit, job and services, low remediation cost, and high density) can potentially reduce the transportation sector's impact on the environment by lowering vehicle kilometers traveled (VKT) and related GHG emissions. This study examines the effect of residential brownfield developments on VKT reduction and the resulting costs (including the cost of driving time, fuel, and external air pollution costs) and further compares the resulting costs with the initial one-time cleanup cost of brownfield sites. Sixteen brownfield and conventional development sites were analyzed in Baltimore, Chicago, Minneapolis, and Pittsburgh. Travel demand models were used to estimate VKT differences among the developments. Air pollution valuation data were used to estimate external environmental cost differences. On average, residential brownfield developments reduce VKT by 52% compared to conventional greenfield developments. Also on average, brownfield developments result in a time and fuel cost reduction of 60% and an external environmental cost saving of 66%. Comparing these cost savings with the initial one-time cleanup cost of brownfields, it is shown that development density and the cost of remediation significantly affect the number of years required for the VKT cost savings to offset the remediation cost. [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]

A stated preference (SP) approach is explored for valuation of travel time displayed as traffic information on a variable message sign (VMS) board. Two types of traffic information (called as TI-I and TI-II) are investigated and the valuation is carried out separately for private car and taxi users. Instantaneous travel time is considered as traffic information in TI-I, while instantaneous travel time along with its variation level is considered as traffic information in TI-II. The SP (choice based) data are analyzed by multinomial logit and random parameter logit (RPL) models. In RPL models, the random parameters are assumed to follow constrained triangular distribution, where mean equals its spread. The decomposition of preference heterogeneity around the mean of random parameter is also investigated in RPL models. A variation is observed in the values of travel time under TI-I and TI-II, highlighting the role of the content of VMS based travel-time information. Both travel time as traffic information and a lower variation level associated with travel time as traffic information are perceived as benefits by trip makers. The perceived benefits likely to be derived by providing travel-time information are found to be distinctly different for private car and taxi users. The study also highlights the role of modeling technique and model specification in the valuation of travel time. [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]

A computational protocol for particle tracking in wellfields with lakes is proposed. The protocol is demonstrated in the Northwest Wellfield, which is the largest wellfield in Florida and contains many lakes. Comparison of the results with existing regulatory travel-time contours shows that the proposed method produces more realistic results in terms of reflecting the presence of lakes. [ABSTRACT FROM AUTHOR]

This paper is concerned with modification and validation of a recently developed microsimulation model of heterogeneous traffic flow and application of the model to study the impact of provision of reserved bus lanes on urban roads. The impact of introduction of an exclusive bus lane is measured in terms of reduction in speed of other categories of motor vehicles, due to the consequent reduction in road space, over a wide range of traffic volume. The main finding of this paper is that if an exclusive bus lane is provided under highly heterogeneous traffic conditions (prevailing in Indian cities), then the maximum permissible volume to capacity ratio that will ensure a level of service of C for the traffic stream comprising all the motor vehicles, except the buses, in a typical eight lane divided urban road (14.5-m-wide road space per direction) is about 0.62. Through this study, a framework for justification of providing exclusive bus lane has also been defined. [ABSTRACT FROM AUTHOR]

It is quite useful to incorporate the transport parameters directly into the space development (SD) model for deciding the location of economic activities. Also, slum developments need special treatment in SD modeling as there are special ongoing drives for rehabilitation of slum population in developing regions resulting in fast changes in SD. The main objective of the present work is to develop a methodology for the development of a SD model for a developing region. The proposed methodology is tested by applying it to simulate the SD in the Mumbai Metropolitan Region (MMR) in India. Aggregate estimation procedure is adopted for estimation of logit allocation functions. The travel time of the zones from nearest railway stations is found to have a strong influence on SDs in MMR. The model is validated by comparing the estimated and observed space transformation and land consumption quantities at ward level. The proposed methodology has simulated SD and redevelopment successfully for MMR. [ABSTRACT FROM AUTHOR]

The time of concentration equation used in Pima County, Arizona, includes a hydrologic parameter representing the impedance to flow for peak discharge estimation on small [typically less than 2.59 km2 (1 mi2)] semiarid watersheds. The impedance-to-flow parameter is similar in function to the hydraulic Manning’s n roughness coefficient in the kinematic wave time of concentration equation; however, the impedance to flow is a hydrologic parameter representing all portions of a watershed rather than a hydraulic parameter representing friction loss during uniform flow. To relate the impedance-to-flow parameter to physical watershed characteristics, impedance-to-flow values were calculated for return period and observed events on five undeveloped rangeland watersheds and correlated with Manning’s n roughness coefficients determined from particle size analysis and simulated flow conditions. Impedance to flow displayed a positive trend with observed peak discharge on each watershed. The results indicate that local impedance-to-flow values can be developed for time of concentration equations using observed rainfall and runoff data, as well as measurable field characteristics. The impedance-to-flow parameter allows for a physical basis in time of concentration estimation without the additional detail of a physically based model. [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]