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This paper points out questionable derivations for the formulated solution in the algebraic approach for the optimal length of local bus route of a traffic model for bus service zones. It also provides revisions for the algebraic approach to prove the existence and uniqueness for the formulated optimal solution. An iterative method was then applied to obtain an alternative sequence that converges to the optimal solution. The same numerical examples in the literature are examined to support the findings. [ABSTRACT FROM AUTHOR]

This paper establishes a mathematical model that can represent the conflicting effects of two pedestrian streams that have an oblique intersecting angle in a large crowd. In a previous paper, a controlled experiment in which two streams of pedestrians were asked to walk in designated directions was used to model the bidirectional pedestrian stream of certain intersecting angles. In this paper, the writers revisit that problem and apply the Bayesian inference method to calibrate an improved model with the controlled experiment data. Pedestrian movement data are also collected from a busy crosswalk by using a video observation approach. The two sets of data are used separately to calibrate the proposed model. With the calibrated model, the relationship between speed, density, and flow is studied in both the reference and conflicting streams, and a prediction is made regarding how these factors affected the interactions of moving pedestrian streams. It is found that the speed of one stream not only decreases with its total density, but also decreases with the ratio of its flow relative to the total flow, i.e., the speed of the pedestrians decreases if their stream changes from the major to minor stream. It is also observed that the maximum disruption that was induced by pedestrian flow from an intersecting angle occurs when the angle is approximately 135°. [ABSTRACT FROM AUTHOR]

Traffic flow forecasting, especially the short-term case, is an important topic in intelligent transportation systems (ITS). This paper researches network-scale modeling and forecasting of short-term traffic flows. First, the concepts of single-link and multilink models of traffic flow forecasting are proposed. Secondly, four prediction models are constructed by combining the two models with single-task learning (STL) and multitask learning (MTL). The combination of the multilink model and multitask learning not only improves the experimental efficiency but also improves the prediction accuracy. Moreover, a new multilink, single-task approach that combines graphical lasso (GL) with neural network (NN) is proposed. GL provides a general methodology for solving problems involving lots of variables. Using L1 regularization, GL builds a sparse graphical model, making use of the sparse inverse covariance matrix. Gaussian process regression (GPR) is a classic regression algorithm in Bayesian machine learning. Although there is wide research on GPR, there are few applications of GPR in traffic flow forecasting. In this paper, GPR is applied to traffic flow forecasting, and its potential is shown. Through sufficient experiments, all of the proposed approaches are compared, and an overall assessment is made. [ABSTRACT FROM AUTHOR]

A toll-lane selection process at a toll plaza can be looked at as an outcome of the choice processes of individual drivers. In this paper, an attempt is made to develop a random utility-based discrete multinomial choice model for the behavior of automobile drivers while selecting toll lanes at a toll plaza. Specifically, a multinomial logit model is developed and calibrated using disaggregate level choice data from three toll plazas with different geometry and rates of arrival of vehicles. The calibrated logit models from the different sites when statistically compared show that a generic model, applicable to all the sites, is possible. Such a generic model is also developed. The use of the proposed model can improve the analysis of traffic flow at toll plazas and ultimately lead to more effective designs of these facilities. [ABSTRACT FROM AUTHOR]

Until recently, the Poisson process has been used to model internet and road traffic queues. It has been established that internet traffic exhibits self-similarity, which is very different from the Poisson process. Motivated by this, efforts have been made to examine whether road traffic is also self-similar. Earlier efforts in this direction indicate that road traffic is indeed self-similar. To substantiate this, this paper examines, by various methods, whether real time traffic on a busy national highway in India is self-similar. The results from this examination prove that the traffic observed on the highway is self-similar. This paper also presents a novel method based on percentiles for computing the Hurst parameter, which is an indicator for the intensity of self-similarity. The paper also validates the percentile method with two other existing methods. Additionally, the traffic at a toll plaza on the highway has been modelled as queueing system, and performance measures have also been computed, namely, mean queue length and busy period distribution. The numerical results clearly demonstrate that the analysis presented in this paper can be useful for improved designs of toll plazas. [ABSTRACT FROM AUTHOR]

Although auxiliary through lanes (ATLs) are often used to increase the capacity at signalized intersections, their utilization by motorists tends to be significantly lower than continuous through lanes (CTLs). This study was designed to (1) determine whether recently proposed models for predicting lane-drop utilization are applicable to Buffalo, New York; (2) develop new models for predicting ATL utilization and for predicting drivers' merge behavior at ATLs; and (3) assess the ability of microscopic traffic simulation models to reproduce observed merging behavior. To achieve this, geometric, traffic count, and merge data were collected from select sites in Buffalo. The study shows that lane utilization in Buffalo is a function of total through-traffic volume, right-turning volume, and upstream and downstream land-use types. The average merge distance, in contrast, tends to be a function of the ATL total length, the first lane-drop warning sign, the speed limit, and the total through-traffic volume. The study also identified the challenges of calibrating merge behavior in microscopic traffic simulation models against field observations. [ABSTRACT FROM AUTHOR]

Traffic flows around roundabouts have been found to be dependent on origin-destination flows, but the true nature of this relationship is not properly understood. Present analyses are based on either gap acceptance models or empirical models. These models do not properly account for the impact of origin-destination flows on roundabout operations. This has limited the possibility to develop strategies that improve roundabout operations by controlling inflows. This research proposes a theory to analyze roundabout traffic flows and a strategy to determine inflows into a roundabout that would maximize the outflow from the roundabout. This strategy could be implemented through use of signals to meter vehicles at the entry. To achieve this, a theoretical framework is proposed based on the macroscopic fundamental diagram for urban networks. The theory and strategy is then tested using microscopic simulation. It was found that the outflow from a roundabout is dependent on the average flow and the average trip length around the roundabout. The average trip length is a function of the origin-destination flows. [ABSTRACT FROM AUTHOR]

Explaining pedestrians' crossing behavior along entire trips may contribute toward more efficient and targeted planning of pedestrian facilities in urban areas and more accurate consideration of pedestrian safety. Although existing research on pedestrians' crossing behavior is extensive, most related studies examine pedestrians' crossing decisions at local level and focus on a particular set of determinants (roadway environment, traffic conditions, or human factors). On the contrary, crossing behavior along entire trips is seldom explored. This paper presents a theoretical framework for modeling pedestrians' crossing behavior along a trip, addressing a large part of the difficulties involved in collecting the necessary data and setting up a modeling framework. First, a topological approach of pedestrian trip characteristics and crossing decisions is proposed, allowing consideration of distinct patterns of crossing behavior along a trip. Moreover, specific techniques from the family of discrete choice models are proposed for determining the number and location of pedestrians' crossings, accounting for the hierarchical and dynamic nature of pedestrians' decisions along a trip. Finally, a field survey method is presented, allowing collection of detailed information about pedestrians' crossing decisions along urban trips, including data on roadway, traffic, and individual characteristics, as well as the interactions between pedestrians and motorists. Preliminary results from a pilot implementation of the proposed framework are promising, and a full-scale application for testing and validation is in progress. [ABSTRACT FROM AUTHOR]

The objective of this research is the analysis of perceived highway level of service in relation to drivers' personal characteristics and traffic conditions. A field survey was carried out for that purpose, in which drivers were asked to assess the traffic conditions experienced on a freeway segment, just after having actually driven along this segment. At the same time, traffic counts were taking place. Exploratory analysis revealed a large dispersion in drivers' assessment of level of service for each volume-to-capacity (v/c) value, especially at moderate traffic conditions. Furthermore, the effect of driver's age, gender, driving experience, familiarity with the road, vehicle capacity, and v/c on perceived level of service was examined by means of nonparametric statistical tests. It was found that, in this sample, only traffic conditions affected drivers' assessment of level of service. Perceived level of service was then modeled against v/c by means of a piecewise linear regression technique for three scenarios, covering different types of drivers with respect to their tolerance of congestion. The results confirm that the relationship between perceived level of service and traffic conditions has a piecewise linear form with significant differences in slopes and breakpoints. Moreover, they suggest that drivers perceive no more than two or three levels of traffic conditions. More specifically, only low-tolerance drivers appear to distinguish level of service A from B and only high-tolerance drivers appear to distinguish level of service D from E. On the contrary, an impressive reduction of perceived level of service occurs within the v/c interval of 0.55-0.70 in all scenarios. These findings are further discussed in relation to their accordance with the highway capacity manual approach for level of service and its possible adjustments. [ABSTRACT FROM AUTHOR]

Traffic simulation modeling has been applied to many transport planning and traffic engineering situations. The further development of traffic simulation modeling to study the impact of safety measures in a systematic, rigorous, and transparent fashion is becoming increasingly viable as the models improve and the understanding of driver behavior is improving. This paper presents an application of traffic simulation to the study the safety problem of heavy vehicle red-light running at a vehicle actuated highway intersection in a metropolitan area in Australia. The processes of data collection, model calibration and validation, and evaluation are described. Modeling driver stop-or-go behavior of drivers of heavy and light vehicle in the “dilemma zone” of the vehicle actuated signal is also illustrated, along with the modeling of surrogate safety measures. Five alternative signal treatments intended to reduce heavy vehicle crash risk were considered. The results of a comparison against the existing situation showed that an extension of amber time was the most effective short-term treatment. Over the long term, however, this treatment is likely to be subject to behavioral adaptation. A green extension for heavy vehicles detected in the dilemma zone and an all-red extension for potential red-light runners were found to be treatments likely to provide a sustainable safety improvement with little impact on operational efficiency. This type of safety modeling is considered to have great potential in the future. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of modeling and predicting urban traffic flow variability, which involves considerable implications for the deployment of dynamic transportation management systems. Traffic variability is described in terms of a volatility metric, i.e., the conditional variance of traffic flow level, as a latent stochastic (low-order Markov) process. A discrete-time parametric stochastic model, referred to as stochastic volatility (SV) model is employed to provide short-term adaptive forecasts of traffic (speed) variability by using real-time detector measurements of volumes and occupancies in an urban arterial. The predictive performance of the SV model is compared to that of the generalized autoregressive conditional heteroscedasticity (GARCH) model, which has been recently used for the traffic variability forecasting, with regard to different measurement locations, forms of data input, lengths of forecasting horizon and performance measures. The results indicate the potential of the SV model to produce out-of-sample forecasts of speed variability with significantly higher accuracy, in comparison to the GARCH model. [ABSTRACT FROM AUTHOR]

A detection scheme that uses classical and spatial statistics has been developed to identify roadways with the most severe safety needs. It is based on the null hypothesis that all roadways have the same crash risk, that is, all have the same nonfatal and fatal crash rates throughout the entire study region. Fatal and nonfatal crash rates, which are assumed to be randomly distributed as Poisson processes, are modeled with a marked homogeneous Poisson process model. Since the traffic exposures are typically unknown at the crash sites, they are predicted with a geostatistical model. Locations, where the null hypothesis is rejected, are safety treatment candidates. P-value risk rankings are used to identify locations with the most severe safety needs. The results from alternative analyses—one using vehicle miles traveled and another using population as measures of traffic exposure—are conducted and compared. The state of New Hampshire is used in a case study. The effects of analyzing areas with small traffic exposures, the so-called “small-area estimation” problem, and the aim to develop a data-driven detection scheme that meets the requirements as an objective decision-making tool are discussed. [ABSTRACT FROM AUTHOR]

There is an ongoing debate among transport planners and safety policy makers as to whether there is any association between the level of traffic congestion and road safety. One can expect that the increased level of traffic congestion aids road safety and this is because average traffic speed is relatively low in a congested condition relative to an uncongested condition, which may result in less severe crashes. The relationship between congestion and safety may not be so straightforward, however, as there are a number of other factors such as traffic flow, driver characteristics, road geometry, and vehicle design affecting crash severity. Previous studies have employed count data models (either Poisson or negative binomials and their extensions) while developing a relationship between the frequency of traffic crashes and traffic flow or density (as a proxy for traffic congestion). The use of aggregated crash counts at a road segment level or at an area level with the proxy for congestion may obscure the actual relationship. The objective of this study is to explore the relationship between the severity of road crashes and the level of traffic congestion using disaggregated crash records and a measure of traffic congestion while controlling for other contributory factors. Ordered response models such as ordered logit models, heterogeneous choice models, and generalized ordered logit (partially constrained) models suitable for both ordinal dependent variables and disaggregate crash data are used. Data on crashes, traffic characteristics (e.g., congestion, flow, and speed), and road geometry (e.g., curvature and gradient) were collected from the M25 London orbital motorway between 2003 and 2006. Our results suggest that the level of traffic congestion does not affect the severity of road crashes on the M25 motorway. The impact of traffic flow on the severity of crashes, however, shows an interesting result. All other factors included in the models also provide results consistent with existing studies. [ABSTRACT FROM AUTHOR]

This paper describes the application of previously developed community-based, macrolevel collision prediction models (CPMs) to evaluate the road safety of a regional transportation plan. The research objective was to present and test model-use guidelines in a regional road safety planning application. The data was extracted from over 400 Greater Vancouver neighborhoods in British Columbia, Canada, including output from the regional transportation model. The CPMs predicted a lower mean collision frequency region-wide due to a proposed three-year transportation plan, versus a do-nothing scenario. Recommendations have been made for future use of the CPMs in regional road safety planning applications. The application of macrolevel CPMs to this regional case study proved a solid step in the development of new and improved empirical tools for planners and engineers to include road safety in the planning process. It is hoped that these models and model-use guidelines will facilitate improved decisions by community planners and engineers, and ultimately, facilitate improved neighborhood traffic safety for residents and other road users. [ABSTRACT FROM AUTHOR]

A method is presented by which traffic flow estimation between known origins and destinations can be evaluated based on a modified entropy model, and by which bus-routing optimization can be performed. The traffic flow analysis is performed by use of an entropy-based formulation of the vehicular movements of students within the domain under examination, while the perceived level of disorder caused by the numerous vehicle-student-trips in the domain under examination is subsequently used for the formulation of a policy and a bus-routing scheme in order to minimize the original entropy in the system. The entropy metric used in the scheduling optimization is related to the probability of student-trips by origin and destination, and an application of the method is illustrated via a case study of an urban university (with facilities in multiple locations) initiating bus service for its students. [ABSTRACT FROM AUTHOR]

The Highway Capacity Manual 2000 indicates that the capacity of two-lane two-way highways (TLTWHWs) is 1,700 pcph for a single direction and 3,400 pcph for both directions. There are no capacity estimates provided for different traffic conditions such as varying percentages of no-passing zones and varying traffic distributions between the two directions of travel. Previous research has reported that capacity conditions for TLTWHW are very difficult to observe in the field while none of the existing simulation models can report capacity for TLTWHW. This paper briefly describes a new TLTWHW simulation model called TWOSIM that is developed, verified, compared to TWOPAS and highway capacity software (HCS) and used for the estimation of capacity for TLTWHW under a variety of prevailing traffic and geometric conditions. These include percentage of traffic per direction, presence of a passing zone, a horizontal curve, a driveway, an upgrade, and heavy vehicles. Capacity of TLTWHW under base conditions is estimated to be 1,800 pcphpl at 64 kph (40 mph) average free-flow speed, 2,000 pcphpl at 80 kph (50 mph), and 2,100 pcphpl at 96–112 kph (60–70 mph). The presence of passing zones was not found to have an effect on capacity. When a driveway or horizontal curve or upgrade is present, the capacity reduction ranges between 3 and 30% when there are no trucks and may reach up to 40% when trucks are present in the traffic stream. [ABSTRACT FROM AUTHOR]

This paper studies traffic features in a mixed traffic flow composed of motorized vehicles and nonmotorized vehicles near a bus stop within a completely different framework. Our work has taken the nonlane based behaviors of nonmotorized vehicles into account, which has seldom been considered in previous works. We have investigated flow rates as well as passenger transport capacity of the system. The traffic state diagram and the spatiotemporal pattern are presented as the inflow rates of motorized vehicles and nonmotorized vehicles change. A nonmonotonic change of passenger transport capacity is also identified at specific parameters. The influence of parameters such as stopped time of buses is discussed. These results are expected to shed some light on the management and design of urban mixed traffic systems. [ABSTRACT FROM AUTHOR]

Two continuous statistical distribution models, gamma and lognormal, are proposed for desired time gap (TX) and time headway (T) of drivers in a steady car-following state on two-lane roads under mixed traffic conditions. A simulation program was developed for traffic movement on two-lane roads incorporating actual behavior of mixed traffic in terms of speed, placement, arrival pattern, and overtaking behavior. The program was run at 50/50 directional distribution with varying traffic composition and volume level. Headway data obtained from simulation runs are analyzed to develop a relationship between the desired time gap (TX) and the speed of the vehicles for five categories of vehicles, namely, car, heavy vehicle, motorized two-wheeler, three-wheeler, and tractor. It is observed that the desired time gap of a two-wheeler is the minimum of all other categories of vehicles. It is due to their better maneuverability, which allows them to maintain shorter gaps. The desired time gap for tractors is the maximum of all vehicles, which is due to their low acceleration capability and poor braking efficiency. These relationships will be extremely useful in developing the microsimulation models of traffic flow on two-lane highways. [ABSTRACT FROM AUTHOR]

The paper develops a short-term space-time traffic flow forecasting strategy integrating the empirical-based seasonal autoregressive integrated moving average (SARIMA) time-series forecasting technique with the theoretical-based first-order macroscopic traffic flow model—cell transmission model. A case study in Dublin city center which has serious traffic congestion is performed to test the effectiveness of the proposed multivariate traffic forecasting strategy. The results show that the forecasts at the junctions only deviate around 10% at a maximum from the original observations and seem to indicate that the proposed strategy is one of the effective approaches to predict the real-time traffic flow level in a congested network especially at the locations where no continuous data collection takes place. [ABSTRACT FROM AUTHOR]

This paper quantifies the influence badly parked vehicles and on-street parking maneuvers have on average link journey times as a function of the duration of the events and the number of designated maneuvers and flow, by applying an M/M/∞ queueing model in which arrival and departure are all Poisson processes. The method has been validated using microsimulations calibrated by in-situ measurements taken in the streets of the city of Santander (Cantabria, Spain). The analysis on the delays shows a good fit for the M/M/∞ model for flows of 60–70% of capacity where the error is always lower than 5%. This demonstrates the efficiency of the M/M/∞ model for studying how on-street parking maneuvers and badly parked vehicles influence traffic flow and avoids the need to use generally more laborious microsimulation models. Microsimulations are used to calculate the reduction in link capacity for each case in the study and the increases in average journey times for the rest of the road users. This shows the effect that allowing on-street parking on arterial or main roads has on the rest of the traffic. [ABSTRACT FROM AUTHOR]

Modeling traffic at toll collection facilities can assist in computing plaza capacity and performance. It may help planners and operators decide on which lane configurations are appropriate for specific hourly approach volumes and vehicle types. On a network of toll roadways, it can be used to identify bottleneck locations. This is accomplished by comparing a plaza’s capacity to the capacity of a highway segment’s just downstream. Both the characteristics of the approaching traffic as well as the roadway conditions before and at the plaza come into play. Choosing a lane configuration with an adequate number of dedicated electronic toll collection lanes that match the approaching traffic demand is one key to maximizing the toll plaza throughput. The capacity of a toll lane is simply the lane’s average hourly processing rate under queuing conditions as measured in the field. Measurements must take place when there is uniformity in the type of toll collected in the lane as well as uniformity in the type of vehicle being processed. The average hourly processing rates for several categories of traffic and tolling type have been measured in the field and have been collected for this study. Complexity is added to the model when configuring a mixed lane that either provides several methods of toll collection, or provides the same toll collection type to several different categories of customers, or provides toll collection in a manner that is a mixture of these two. SHAKER was developed to address these issues and compute capacities of toll plazas that consist of mixed lanes as well as dedicated lanes. This study also applied the SHAKER methodology to 5 real toll facilities plazas on the Florida Turnpike. The model was both calibrated and validated using these data. [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]

The paper presents a methodological framework that integrates different data-driven techniques in order to detect the different traffic flow regimes (free flow, congested conditions, and so on) observed in signalized arterials as well as the manner traffic flow shifts from one regime to another (transitions). Traffic flow is determined by the joint consideration of the temporal evolution of volume and occupancy. The boundary conditions of the different regimes are identified via a fuzzy wavelet approach based on the volume-occupancy relationship. Moreover, a Bayesian network is developed in order to discover hidden associations between the observed shifts and the traffic flow conditions they occur. Results from the data-driven approach indicate the existence of four distinct traffic flow regimes; these regimes hold in arterials with different geometric and signalization characteristics. Finally, results are further discussed via an analytical model based on the kinematic wave theory; the comparative study of both approaches provides strong evidence that the presented statistical framework is in agreement with a simple and elegant analysis including traffic parameters that are observable and measurable. [ABSTRACT FROM AUTHOR]

In recent years, the techniques for screening transportation networks to identify high crash locations have become more sophisticated with significant data requirements. This paper presents the results of an empirical analysis of screening and ranking for weather related crashes on rural 1.6 km (1 mi) highway sections of Oregon highways. The analysis includes data generated with the extensive use of spatial techniques and incorporates climate data to enhance environmental considerations. The paper compares the results of five ranking methods: Critical rate (by functional class), critical rate (by functional class and climate zone), potential for crash reduction, expected frequency (adjusted by empirical-bayes), and frequency. For the empirical-bayes methods, safety performance functions were generated using negative binomial regression techniques. The 20 top 1.6 km (1 mi) sections were identified for each method and compared. The results reveal that the frequency and expected frequency methods identified the most sites in common, followed by the rate-based methods. The potential for the crash reduction method identified the most unique ranked list. The results highlight the differences in ranking methods. [ABSTRACT FROM AUTHOR]

The continuity equation involving traffic flow expresses the relationship between density, flow, and speed. Density equals the flow divided by space mean speed. The formulation of this equation in the year 1952 has two important assumptions. One assumption is that spacing and speed are constant, i.e., uncongested conditions with moderate to slightly high volumes. The other assumption is that homogeneous traffic prevails, vehicle composition is uniform and vehicles behave within strict lane discipline rules. To determine if the continuity equation is valid under nonhomogeneous traffic conditions, one performs an experiment involving data collection of density, flow, and speed at three midblock sites in India. Data collection occurred when uncongested conditions prevailed with moderate to slightly high volumes. Comparing the average density derived from observed densities in the field to the density derived from the continuity equation reveals whether or not the continuity equation accurately predicts average density under nonhomogeneous traffic conditions. Similar traffic operating characteristics served as the basis for grouping vehicles into five traffic entity types. The association between average density based on observed densities of nonhomogeneous traffic and density derived from the continuity equation had a correlation coefficient of +0.88 for light, four-wheeler type, +0.85 for heavy vehicle type, +0.90 for motorized, three-wheeler type, +0.83 for motorized, two-wheeler type, and +0.50 for nonmotorized, two- and three-wheeler type. Additionally, a nonparametric test, i.e., Wilcoxon signed-rank test, compared observed and derived densities. At a 95% confidence level, no significant difference existed between observed and derived densities of light four-wheeler type, heavy vehicle type, motorized two-wheeler type, nonmotorized two- and three-wheeler type, and between cumulative observed and cumulative derived density. Only in the case of the motorized three-wheeler type, the observed, and derived densities are significantly different. These moderate to strong correlations coupled with the results of the nonparametric test validate the application of the continuity equation when traffic is nonhomogeneous. [ABSTRACT FROM AUTHOR]

Future deployments of transit signal priority (TSP) in the United States depend largely on improving TSP strategies to better accommodate transit vehicles while at the same time minimizing the negative impacts on the vehicles of the nonprioritized approaches. Advanced vehicle location (AVL) technology holds great potential in this regard. This paper develops a methodology that incorporates the predicted bus arrival time information into an AVL based TSP system to improve its performance. It is demonstrated analytically that the time to trigger the traffic signal for priority operation, especially Early Green, is of particular importance to both transit and passenger vehicles. A theoretical model is developed to identify the optimal time to place a priority call based on the predicted bus arrival time information. A simulation analysis is conducted to verify the theoretical approach and further identify the optimal call-time points for general cases. The research is focused on operation of TSP under moderately congested and congested traffic conditions wherein the concern about the adverse impact of TSP exists. It shows that in general, starting the priority operation when the bus is about 20–30 s away from the intersection produces good results for both bus and general traffic. The findings of the research can be easily integrated into an AVL based TSP system and may potentially enhance the performance of such a system. [ABSTRACT FROM AUTHOR]

User-equilibrium (UE) traffic assignment is widely used in travel forecasting models. Analyses of assignment results often require detailed information about route flows. For a given UE model, total link flows are uniquely determined, but there can be many route flow solutions. Our goal is to evaluate the potential disagreement between different route flow solutions for the same UE model. We considered a case study of a UE model for the Chicago region. We examined 1,000 different UE route flow solutions. Out of 127,248 routes used by UE solutions, 53,134 have alternatives. For 51,748 routes (97%) the flow is completely undetermined, meaning that it is zero in one UE solution and equal to the total origin-destination (O-D) flow in another UE solution. Significant differences were also found in the distribution of users of a given link among O-D pairs (“select link analysis”). We conclude that in analyses that require route flows, it is important to make a proper choice of a specific route flow solution, such as the maximum entropy user equilibrium route flow solution. [ABSTRACT FROM AUTHOR]

The Highway Capacity Manual (HCM) defines uniform delay as a portion of control delay and estimates it based on the flow diagram originally developed for a protected movement. This paper demonstrates the shortcomings of the HCM delay model in the estimation of uniform delay of permitted left turns from an exclusive lane (PLTEL), and proposes a new uniform delay model developed for PLTEL. The control delays estimated by the original HCM model and the one estimated by the proposed uniform delay model for PLTEL were compared against those from corridor simulation. Results showed that the HCM model tended to underestimate uniform delay, and the proposed model for PLTEL showed improved results. [ABSTRACT FROM AUTHOR]

The paper improves upon the Yu and Van Aerde calibration procedure of the TRANSYT-7F platoon dispersion model, particularly for time steps greater than 1 s in duration, and develops three generalized platoon dispersion models that explicitly account for the effect of the time step duration on traffic dispersion. The paper then validates these models utilizing two datasets: the first includes field data gathered in Montréal, while the second was generated using INTEGRATION microscopic traffic-simulation software. The results demonstrate that the predicted flow profile using the proposed platoon dispersion models provides a good fit to field-observed and simulated profiles, regardless of the modeling time step considered. [ABSTRACT FROM AUTHOR]

The paper demonstrates some inherent limitations of the TRANSYT software with regard to calibration of the recursive platoon dispersion model and, more specifically, modification of the travel time factor. Subsequently, the paper develops a procedure that overcomes this limitation by adjusting the average link travel time to control the value of the travel time factor indirectly. Furthermore, the paper presents numerical examples that provide a preliminary investigation of different calibration procedures of the recursive relationship. Although the dataset used for this purpose was generated using the INTEGRATION microscopic traffic-simulation software, the procedures are general and intended for use with field data. The calibration procedure developed in this paper is demonstrated to produce the best results in terms of simplicity and accuracy. [ABSTRACT FROM AUTHOR]

Many studies focused on the development of crash prediction models have resulted in aggregate crash prediction models to quantify the safety effects of geometric, traffic, and environmental factors on the expected number of total, fatal, injury, and/or property damage crashes at specific locations. Crash prediction models focused on predicting different crash types, however, have rarely been developed. Crash type models are useful for at least three reasons. The first is motivated by the need to identify sites that are high risk with respect to specific crash types but that may not be revealed through crash totals. Second, countermeasures are likely to affect only a subset of all crashes—usually called target crashes—and so examination of crash types will lead to improved ability to identify effective countermeasures. Finally, there is a priori reason to believe that different crash types (e.g., rear-end, angle, etc.) are associated with road geometry, the environment, and traffic variables in different ways and as a result justify the estimation of individual predictive models. The objectives of this paper are to (1) demonstrate that different crash types are associated to predictor variables in different ways (as theorized) and (2) show that estimation of crash type models may lead to greater insights regarding crash occurrence and countermeasure effectiveness. This paper first describes the estimation results of crash prediction models for angle, head-on, rear-end, sideswipe (same direction and opposite direction), and pedestrian-involved crash types. Serving as a basis for comparison, a crash prediction model is estimated for total crashes. Based on 837 motor vehicle crashes collected on two-lane rural intersections in the state of Georgia, six prediction models are estimated resulting in two Poisson (P) models and four NB (NB) models. The analysis reveals that factors such as the annual average daily traffic, the presence of turning lanes, and the number of driveways have a positive association with each type of crash, whereas median widths and the presence of lighting are negatively associated. For the best fitting models covariates are related to crash types in different ways, suggesting that crash types are associated with different precrash conditions and that modeling total crash frequency may not be helpful for identifying specific countermeasures. [ABSTRACT FROM AUTHOR]

Federal, state, and local agencies use vehicle classification data for planning, design, and conducting safety and operational evaluation of highway facilities. In conformity with federal reporting requirements, most states use the “F” scheme which classifies vehicles based on their axle configurations; primarily the number of axles and the length of axle spacings. However, the scheme is prone to errors resulting from imprecise demarcation of class thresholds. To improve classification, the problem is hereby viewed as a pattern recognition problem in which statistical techniques such as probabilistic neural networks (PNN) can be used to assign vehicles to their correct classes. In this research, the network was trained and applied to field data composed of individual vehicle’s axle spacing and number of axles per vehicle. The PNN reduced the error rate by 3.3% compared to an existing classification algorithm. The error rate was further reduced by 6.5% when the individual vehicle’s gross weight was added as a classification variable. These results confirm the promise of neural networks in axle classification but the technique still requires additional field validation as well as exploration of additional variables to improve categorization of vehicles into the F or other schemes. [ABSTRACT FROM AUTHOR]

The paper presents a capacity estimation model for a signalized intersection approach with a short right-turn lane. The proposed model overcomes one of the major shortcomings of the current capacity estimation methodologies by considering the probabilistic nature of traffic flow and the effect of queue blockage to the short-lane section. The research showed that the capacity of a signalized intersection with a short right-turn lane is strongly related to the length of the short lane, the proportion of through and right-turn vehicles, and cycle length. The study also revealed new findings, through simulation, on the saturation flow rate discharging from the single-lane section where the right-turn lane splits. It was found that the saturation flow rate increases with the increase of the length of the right-turn lane. The proposed model was validated against the results from CORSIM, a microscopic traffic simulation model, and found general agreement between the two results. Capacity enhancement is achieved with a short right-turn lane compared to the shared-lane situation. Such a capacity enhancement is contributed by both the short-lane usage and the increased saturation flow rate from the single-lane section. An example of a common application of the proposed model is to provide adequate design of the length of the right-turn lane so that the projected traffic demand can be accommodated at an acceptable service level while minimizing the cost associated with the construction of a full or longer right-turn lane. [ABSTRACT FROM AUTHOR]

To assess highway system performance, states are required to submit compositional traffic count reports to the Federal Highway Administration (FHWA). A methodology was developed for obtaining compositional vehicle counts from low-cost portable video technology. Autoscope, an advanced video camera with machine vision processing, was used to capture, process, and store compositional counts based on vehicle length. A maximum of five length-based vehicle classes can be differentiated from video; therefore a methodology was developed that generates the FHWA 13-category vehicle classes. A disaggregation process, typically used in stochastic hydrology, is employed. Results from the disaggregation model are compared with actual FHWA classification data obtained from axle counters. It was observed that less than 1% of the vehicles were not included in the model output and 3.43% were misclassified. In addition, 95 and 99% confidence intervals calculated by the disaggregation model included 92.3 and 100% of the actual axle counter data. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for extracting vehicular trajectory data using a video-capture technique, and describes a computer-based tool, Vehicle Video-Capture Data Collector (VEVID), which was developed to help extract trajectory data from the video. The proposed methodology consists of three basic steps. First, an urban street is videotaped from an elevated position, and distances between reference points are measured and input into the VEVID parameters file. Second, a segment of video is digitized into a Video for Windows (AVI) file at a user-specified frame rate. Third, the AVI file is registered in VEVID, and then, in each frame, the user simply clicks the mouse over a distinguishable point of targeted vehicles. Trajectories are output into a trajectory data file along with speeds, accelerations, and gaps of targeted vehicles. Finally, several types of data extracted from VEVID that are applicable to microscopic traffic simulation modeling on urban street networks are discussed to show the capabilities of the proposed approach in traffic modeling. [ABSTRACT FROM AUTHOR]

This paper presents a new distribution model for site impact studies. The new model is based on relationships between the site origin–destination trips and the link traffic counts of the surrounding street system. With this model, the new site-generated trips can be distributed on a particular street link by using a correlation coefficient that is evaluated by the likelihood of traffic patterns of the total trips generated from the study area and the inbound or outbound traffic passing through the street link. Unlike the traditional gravity model, the new model does not rely on land-use data to determine the trip distributions, and is therefore well suited to situations where little or no land-use information is available, such as in developing countries. [ABSTRACT FROM AUTHOR]

The study consists of a systematic simulation evaluation of transit signal priority (TSP) impacts on the operations of a single signalized intersection within a coordinated arterial system. The study demonstrates first that in general TSP provides benefits to transit vehicles that receive priority. Second, TSP has a marginal systemwide impact for low traffic demand, although as the demand increases, so do the systemwide disbenefits of TSP. Third, the systemwide impact of TSP is directly proportional to the frequency of transit vehicles. Fourth, TSP impacts are sensitive to the demand distribution at a signalized intersection. Specifically, transit vehicle arrivals on heavily congested approaches may result in systemwide benefits if the conflicting approaches are not congested. Alternatively, transit vehicle arrivals on lightly congested approaches may produce significant systemwide disbenefits if the conflicting approaches are heavily congested. Fifth, the systemwide benefits of TSP are dependent on the phase at which the transit vehicles arrive, especially if the cycle length is maintained within the priority logic. Sixth, the systemwide benefits of TSP are highly dependent on the optimality of the base signal timings. Finally, transit vehicle dwell times at nearside bus stops can have significant systemwide impacts on the potential benefits of TSP. [ABSTRACT FROM AUTHOR]

The percentage of sport-utility vehicles, light-duty trucks, and passenger vans in the on-road light-duty fleet is steadily increasing. The implications for modeling emissions from on-road vehicles are significant since light-duty trucks produce, on average, more emissions than passenger cars. Although not frequently considered in calculating emission rates, differences in the average speeds of passenger cars, light-duty trucks, sport-utility vehicles, and passenger vans may influence emissions. This paper investigated whether the different classes of passenger vehicles operated differently on-road. Spot speed studies were used to determine differences in operating characteristics for three subgroups of the passenger car fleet. Results are presented for studies on freeways and arterials. [ABSTRACT FROM AUTHOR]

The increasing use of roundabouts for plane junctions calls for an evaluation of the potential accident rate for this kind of intersection. This gives a further element for the choice between alternative typologies of intersection for the requalification and the adjustment of road junctions. This paper presents a model for the potential accident rate evaluation in large and medium roundabouts. The model is based on dynamical considerations and on the user’s behavior crossing the intersection. The model’s response to the traffic conditions in the intersection and to capacity formulations is also analyzed. [ABSTRACT FROM AUTHOR]

In this paper a comprehensive microscopic model of driver behavior in uninterrupted traffic flow is developed. The model aims to explain or predict the actions of a driver in a variety of driving scenarios ranging from free flow conditions on wide roads to forced flow conditions on narrow two-way roads. The salient features of the proposed theory are (1) a single model (or framework) is used to describe driver behavior in different driving scenarios and (2) the response of drivers through both steering control and speed control are modeled. Driver behavior is simulated in 19 different driving scenarios. Results from these simulations show the efficacy of the proposed model in explaining/predicting driver behavior. [ABSTRACT FROM AUTHOR]

This paper presents the findings of a study evaluating the potential benefits of implementing transit signal priority along the Columbia Pike arterial corridor, in Arlington, Va. The study uses the INTEGRATION microscopic traffic simulation model to evaluate the impact of a number of alternative priority strategies on both the prioritized buses and general traffic during the morning peak and midday traffic periods. The transit priority strategies considered include providing priority to express buses traveling along Columbia Pike, to both express and regular buses along the arterial, and to all buses within the study corridor. The priority logic that is considered in the study provides simple green extensions and green recalls within a fixed-time traffic signal control environment. The simulation results indicate that the buses provided with priority would typically benefit from transit priority, but that these benefits may be obtained at the expense of the overall traffic, particularly when traffic demand is high. However, it is also found that in periods of lesser demand, the overall negative impacts could be negligible due to the availability of spare capacity at the signalized intersections. [ABSTRACT FROM AUTHOR]