Journal of Transportation Engineering, Part A: Systems

The primary focus of this paper is to develop models to estimate link-level crash frequency using land use data extracted and integrated through the use of a distance gradient method. The on-network characteristics were added to the integrated land use characteristics database and were also used in the development and validation of link-level crash frequency estimation models. Both statistical and back-propagation neural network (BPNN)-based approaches were tested and evaluated for modeling. Mean absolute deviation (MAD), median error, 85th percentile error, and root-mean squared error (RMSE) were computed to validate the developed link-level crash frequency estimation models and compare the two approaches. The results obtained from validation of the link-level crash frequency estimation models indicate that the computed errors are low for models based on both statistical and neural network approaches. Both the approaches have reasonably good predictive capability and can be used to estimate crash frequency. The role of predictor (includes integrated land use) variables on crash frequency along links can be easily understood using outputs from the statistical modeling approach. Also, findings indicate that models based on integrated land use and on-network characteristics (excluding traffic volume) have good predictive capability and can be used as surrogate data to estimate crash frequency if traffic volume data are not available.

This paper presents an innovative design method for determining lateral clearance needs on a spiraled horizontal curve to satisfy sight distance requirements. The roadside lateral clearance is represented by a spiraled horizontal curve that is easy to implement in practice. The design parameters were determined. The characteristics of the corresponding lateral offsets were explored for the influential roadway factors, including required sight distance or design speed, curve radius, curve length, spiral curve length, and station location expressed in fraction of sight distance. The results show that ratio of spiral curve length to required sight distance is the major factor that affects the ratio of the lateral offset to the maximum offset at a circular curve. A single design chart and a design table also are provided as alternative design tools to determine the offset for a specific obstruction location. The proposed design method not only greatly improves the AASHTO approximate approach, but also provides an alternative approach to improving design consistency on horizontal alignments. This paper complements another paper on lateral clearance needs for simple horizontal curves.

Understanding the factors that contribute to crash frequency and severity will assist better highway design and develop appropriate countermeasures for hot spots, thereby improving the safety of the road system. This study explores the influences of risk factors on frequency and severity of large truck–involved crashes. Multinomal logit (MNL) and negative binomial (NB) models are proposed to analyze crash severity and frequency, respectively. The explanatory factors include characteristics of the vehicles, drivers, traffic, environment, and roadway geometric design features. To obtain better parameter estimation results, the maximum likelihood (ML) method and Bayesian method are employed. The results show that the MNL and NB models have a better goodness of fit under the Bayesian estimation framework. Using Bayesian MNL and NB models, factors that significantly affect crash frequency and severity outcomes are analyzed. Some critical factors that contribute significantly to both crash severity and frequency estimation of large truck–involved crashes, including truck percentage, annual average daily traffic (AADT), driver condition, and weather condition, are identified and discussed. Driver age, speed limit, and location type are found to have significant effects only on the frequency of large truck–involved crashes. Seat belt usage, light condition, and terrain type are found to have significant effects only on the severity of large truck involved crashes. The results from this study will be valuable in transportation policy making, improvement of carrier operation, and crash-cost reduction.

Analysis of traffic at a signalized intersection requires quantification of the number of vehicles in the queue and the corresponding delay. Such analyses can be used for optimal signal control as well as for intelligent transportation system (ITS) applications such as advanced traveler information systems (ATISs). However, the direct measurement of these variables is difficult because of their spatial nature. Hence, they are usually estimated using location-based data such as count, speed, occupancy, and so on that can be obtained from point-based detectors such as loop detectors installed on roads. However, driving maneuvers such as lane shifts and free right turns can lead to inaccurate queue estimates in lane-based analysis. To check this, analysis of lane-based data was carried out and discrepancies in the count data obtained from loop detectors were observed. To address these issues, the present study proposed a model-based queue estimation scheme using the Kalman filtering technique, taking into account the statistical properties of detector errors. The detector data and the signal timing information were used as inputs in the estimation scheme. The estimation was carried out for two cases—one where the queue ends within the advance detector and one in which the queue extends beyond the advance detector. Field data collected from four different intersections were used to corroborate the estimation scheme for the “queue within advance detector” scenario. Because of the lack of availability of field data for “queue beyond advance detector,” simulated data were used to corroborate the corresponding results. Results showed that the estimation scheme that incorporated the statistical properties of the detector errors performed better than the scheme that did not incorporate errors.

Curvature due to temperature curling and moisture warping has a large effect on the performance and assessment of jointed plain concrete pavements (JPCP). Accurate curvature prediction can improve the design and evaluation of these structures. Daily changes in curvature are often assumed to be solely dependent on the temperature. However, data from instrumented slabs indicate that a hysteresis loop occurs on the equivalent linear temperature gradient (ELTG) versus curvature plot. A method is introduced to characterize the hysteresis loop by a characteristic height. This parameter is found to follow long-term and seasonal patterns. The characteristic height is correlated to the amplitude of the daily relative humidity cycle, indicating that the hysteresis loop may be affected by daily moisture movement within the slab. A method is introduced to identify the components of strain independent of temperature and restraint. These temperature-independent and restraint-independent strains are used to probe the long-term and seasonal changes in the hysteresis behavior.

This paper examines the application of power spectral density (PSD) in turnout geometrical degradation analysis. Power spectral density in the form of a continuous curve can show track irregularities through wavelength and amplitude. The longitudinal level parameter measurements of four turnouts from Swedish railways are selected. ProVal software is used for PSD implementation. The PSD curves of the actual measurements are compared to PSD standards programmed in MATLAB. The results show that PSD can demonstrate turnout geometrical condition by means of wavelength and spectral density, providing more information regarding the nature of faults in turnouts. It is also able to demonstrate the condition of turnout sections separately and determine which section needs corrective maintenance. Thus, using PSD helps identify the need for corrective maintenance more precisely; this type of maintenance is important because it deals with safety-related defects. The knowledge obtained by PSD is helpful when planning corrective maintenance activities to rectify geometrical faults in turnouts of a line, reducing the time and money spent on maintenance.

This study investigated pedestrian jaywalking at signalized crosswalks. Observational surveys were conducted at seven crosswalks in different areas in Hong Kong, after which pedestrian information and site condition data were incorporated into a database. A binary logit model was used to identify possible factors that determine the probability of pedestrian jaywalking. To address the variation in the effects of the explanatory variables among pedestrians and the unobserved heterogeneity across sites, a random parameter model and a random effect model were used, respectively. The results showed that the random parameter model performed the best in terms of goodness of fit. It was found that the signal when a pedestrian arrives at the crosswalk is critical for decision making, and the jaywalking of surrounding pedestrians also influences the pedestrian’s decision to cross. The gender and walking speed of the pedestrian, vehicle flow, and site location and condition of the crosswalk were also found to significantly determine the probability of pedestrian jaywalking.

Railway track maintenance management systems are currently based on track geometry indexes with the aim of achieving the required level of track safety. Track passenger ride comfort (PRC) has not been taken into account in the current track maintenance methods, making the effectiveness of the conventional approach questionable particularly with the new generation of high-speed railway tracks. The rapid development of high-speed tracks throughout the world, which increases the importance of the track PRC, has created an urgent need for the consideration of PRC in the maintenance of railways. This paper develops a new railway track conditions index which takes into account PRC. Development of the new index is performed based on statistical analyses of the results obtained from extensive field studies. Using of the new index, the current maintenance approach is improved by developing a new algorithm for prioritizing and scheduling maintenance activities. The advantages of the new maintenance approach are illustrated by comparisons of its outputs with those of the current approach. It is shown that the new proposed maintenance algorithm ensures not only the track safety but also the required level of PRC.

Vehicle miles of travel (VMT) data have a wide range of applications in highway agency business processes. However, at all administrative levels, highway agencies continue to be stymied by the poor reliability of local-road VMT estimates resulting from the inadequacy of local-road traffic counts. This paper presents a methodology to address this problem. The methodology first clusters local roads and then imputes traffic-volume data for segments within each cluster by applying spatial interpolation techniques and sparse traffic-volume data. The proposed methodology uses geographic information system (GIS)-enabled spatial interpolation algorithms, including Kriging, inverse distance weighting (IDW), natural neighbor (NN), and trend techniques. The accuracy, in terms of prediction error, of each technique was validated using actual traffic counts. Spatial interpolation techniques can yield efficient imputations of absent traffic data and therefore can produce reliable estimates of local-road VMT. The results in this paper suggest that the use of spatial interpolation for local-road VMT estimation is cost-effective because it makes use of the available traffic-count data from existing road segments and therefore does not require additional data collection efforts. Also, a comparison was made of the relative efficacies of the alternative spatial interpolation techniques for purposes of imputing missing traffic data at certain links and ultimately for VMT estimation or prediction. The methodology can be updated easily with new traffic-count data and can be used by any highway agency for local-road VMT estimation. An essential prerequisite is a comprehensive inventory of the local roads for which the VMT is sought.

The drivers on a minor approach at an unsignalized intersection intending to maneuver are usually at risk because of the difficulty in judging if available gaps are safe or not. Any misjudgment may result in collision with major-stream vehicles. Drivers are clear about rejecting small gaps and accepting large gaps, but experience dilemmas for a range of gaps. This paper is an attempt to study the dilemmas of minor-road vehicles intending to cross the major road. It has been reported in past studies that drivers in India are more aggressive and often accept very small gaps, creating dangerous situations. The authors analyzed variations in spatial gap-acceptance behavior by different drivers and arrived at dilemma-zone boundaries. Field data were collected at four uncontrolled intersections with posted speeds of $40\mathrm{km}/\mathrm{h}$ (medium-speed intersection) and $60\mathrm{km}/\mathrm{h}$ (high-speed intersection). Drivers’ behavior intending to cross a major street is modeled as a binary decision (accept or reject a gap). The distribution of the dilemma zones was analyzed for different speeds and categories of conflicting vehicles and times of day (i.e., day versus night). Speed, vehicle type, and time of day had a statistically significant effect on both the length and the location of the dilemma zone. Dilemma zones can be further used to develop driver assistance systems to improve driver safety by assisting them in making the right decision in critical situations at uncontrolled intersections. Although the study intersections are in India, where road traffic, like that of other developing countries, is characterized by aggressive drivers, the concept and methodology are useful in developed countries, too.

There exist several classes of physical interventions to address congestion at arterials, including widening (construction of additional lanes) and upgrading to expressway standards where possible. For each class of intervention, a key question is the threshold or benchmark traffic volume (BTV) at which the intervention is feasible. For long-term congestion mitigation planning for multiple arterial sections in a network and for planning-phase corridor development, agencies desire to identify BTVs for congestion mitigation interventions. The existing literature addresses this issue largely in feasibility analysis for specific projects, with little or no guidance regarding network-level planning considerations. In addressing this gap, this paper presents a detailed network-level planning methodology that is rooted in economic efficiency. The methodology first establishes three basic alternatives—Do-Nothing, Widening, and Upgrade-to-Expressway—and expresses the total lifecycle cost for each alternative as a function of the current traffic volume in terms of average daily traffic (ADT). The BTV is identified as the ADT at which the lifecycle costs of any two alternatives are equal. This paper demonstrates the application of the proposed methodology using data from in-service roads, and the results indicate that the relative weights assigned to the agency and user costs significantly influence the BTVs. In addition, results are presented for the scenario where these two cost categories are assigned equal weights; for this scenario, widening is never the best option. In the probabilistic situation, it is found that, at an 80% confidence level, it is economically attractive to upgrade a four-lane major arterial to an expressway when the ADT reaches 6,000; at the 100% confidence level, it is economically attractive to upgrade to an expressway when the ADT reaches 19,000.

Delay caused to an individual vehicle at a signalized intersection is a performance measure to assess the level of service. While dealing with delay in Indian traffic conditions, researchers generally opt for either the U.S. Highway Capacity Manual (HCM) or Webster’s delay model. However, these models, developed on the basis of homogeneous traffic conditions, yield erroneous results for developing countries where the traffic is highly heterogeneous with almost no lane discipline. In this context, the present study presents an improved delay model for signalized intersections under heterogeneous traffic conditions. The proposed model has been developed by modifying the existing HCM model on the basis of traffic data collected from seven four-legged signalized intersections across the country. Measured queue length was plotted with time and Simpson’s one-third rule was used to estimate the total delay in a cycle and average delay per vehicle. The proposed model is validated by using the data of eight other intersections and the maximum difference between the observed delay and estimated delay is found to be less than 5%.

The primary purpose of this study is to develop a systematic approach to effectively identifying and ranking recurrent freeway bottlenecks using travel-time reliability (TTR) measures. To achieve this goal, three subtasks are undertaken: (1) to identify recurrent freeway bottlenecks at the network level, (2) to rank discerned freeway bottlenecks, and (3) to examine the impacts of different threshold values used in defining TTR measures. Research results suggest that two TTR measures, namely the frequency of congestion (FOC) and planning time index (PTI), are suited to identify and rank recurrent freeway bottlenecks at the network level. A case study is performed to validate the feasibility of the proposed methodology using vehicle probe data collected on four interstate freeways in Mecklenburg County, North Carolina, in 2015. Suggestions about the selection of threshold values in defining FOC and PTI for freeway bottleneck and congestion analysis are also provided. The findings of this study can provide insightful and useful information for traffic engineers and decision makers in identifying recurrent freeway bottlenecks and in developing effective congestion mitigation strategies for planning applications.

Capturing real-time traffic system characteristics is a primary step in any intelligent transportation system (ITS) application. The majority of the traffic sensors used to capture real-time data have been developed for homogeneous and lane-disciplined traffic conditions. Hence many of them may not perform accurately under heterogeneous and less-lane-disciplined traffic conditions, ultimately leading to reduced estimation accuracy of end applications. The present study addresses this issue by developing an artificial neural network (ANN)–based estimation scheme that can handle these errors and still generate reasonably accurate results. The estimation of location-based speed, stream-based density, and stream speed is carried out using erroneous data as inputs to an ANN trained with accurate data. The same is also performed under varying ranges of errors in inputs. The results show that the ANN can handle the errors in automated data and produce accurate traffic state estimates when trained with good-quality data, hence demonstrating its efficacy for real-time ITS implementation under such traffic conditions.

Information and communication technology (ICT) is becoming ubiquitous, fuelled by the growing popularity of advanced equipment, apps, and high-bandwidth Internet connections. Individuals have been provided with virtual accessibility to a wide range of services by ICT, resulting in transformation of activity and travel behavior of people. This study has analyzed the effects of ICT on activity and travel patterns and estimated the networkwide impact of telework in urban areas based on the data collected from workers from the information technology (IT) capital of India, Bangalore. The analysis was carried out sequentially with factor analysis, structural equation modeling, and urban transport model runs using transport modeling software. Four scenarios were tested for the current and future year. The impact of telework on the transport network characteristics and environmental parameters was assessed. Model results revealed a reduction in vehicle kilometers traveled (VKT) and vehicle hours traveled (VHT) of approximately 1.90 and 3.6% at present, with further reduction to 3.2 and 6.10%, respectively, in next 5 years. Overall transport network delay reduced by a maximum of 6% at present to 9% in a period of 5 years. Peak-hour emissions were reduced for current and future scenarios.

Connected vehicle (CV) technology allows developing next-generation transit signal priority (TSP) that is based on cooperation between signals (change signal timing) and buses (alter speed). In this study, a CV-based TSP that was previously developed and evaluated through a computer simulation was tested in the field. The field experiment at the Smart Road testbed at the Virginia Tech Transportation Institute (Blacksburg, Virginia) validated the feasibility of CV-based TSP and evaluated its performance. The proposed TSP algorithm properly delivered TSP green times to buses with a 100% success rate. In addition, it reduced delays—between 32 and 75%—for a bus traveling approximately $72.4\mathrm{km}/\mathrm{h}$ ($45\mathrm{mi}/\mathrm{h}$) and a traffic signal with a 90-s cycle length with 30-s green time. Moreover, the field experiment showed that regular and differential global positioning system (GPS) devices demonstrate no statistically significant difference in performance. This finding could facilitate large-scale implementation of the TSP logic based on connected vehicle technology (TSPCV) because the regular GPS devices are readily available and much cheaper than differential GPS devices.