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Travel times of freight trucks (or trucks) play a major role in trip planning, identification of efficient routes, and allocating resources for implementation of strategies. While research on travel time estimation models for passenger cars or traffic stream is documented in the literature, their applicability for trucks remains debatable. Truck travel is influenced by the road characteristics, surrounding land use, and demographics of an area. The focus of this research, therefore, is on estimating the truck travel times using the on-network (road) and off-network (land use and demographics) characteristics. Truck travel time data for 501 road links in North Carolina from 2019 were processed for four times of the day and two days of the week. Generalized estimating equations (GEE) were used with the average truck travel time per mile (ATTTPM) as the dependent variable. Spatial proximity (buffers of 0.25 mi, 0.50 mi, and 1 mi) and spatial weights (distance decay functions like 1/d, 1/d2, and 1/d3) were explored to check the best possible approach for capturing off-network characteristics. The office (business park and administrative areas), transportation (rest areas and parking facilities), heavy commercial, and light industrial land uses have an increasing influence on the ATTTPM. The linear model developed using data from a 0.25-mi buffer is best suited to estimate the ATTTPM. Off-peak hour delivery incentives or truck priority systems on road links near these land uses can be implemented to improve truck mobility. The methodology illustrated in this research is transferable and could be used for estimating truck travel times across a region.

Urbanization and automobile-oriented development have resulted in a significant increase in vehicular trips towards central business district (CBD) areas, consequently increasing the demand for on-street parking. In many Indian cities, on-street parking demand is regulated by an odd–even date on-street parking scheme. Under this scheme, sidewalks are subjected to fully and partially confined boundaries, affecting pedestrian movement and the level of service (LOS). Therefore, it becomes imperative to study the influence of on-street parking on pedestrian characteristics to design efficient pedestrian facilities. Pedestrian flow data were collected at three study sites in the CBD areas of three major metropolitan cities of Gujarat, India, using a videography technique. Investigation revealed an 18% reduction in walking speed for parking sidewalk face (PSF) compared to non-parking sidewalk face (NPSF), attributed to the variation in density and presence of parked vehicles. The influence of parked vehicles probed for a similar set of pedestrian densities revealed a 14% reduction in walking speed for PSF compared to NPSF. For the same geometric attributes of the sidewalk, PSF had a deteriorated pedestrian LOS at a given average condition, promulgating the need to develop separate LOS thresholds for PSF and NPSF. Based on the developed speed-density relationships for PSF and NPSF, the Monte-Carlo simulation was performed to facilitate the development of LOS thresholds. Based on the developed LOS thresholds and cumulative percentile plots, the study recommends LOS B as the design service level for sidewalks in the CBD area characterized by on-street parking.

The focus of this paper is two-fold—(1) to research and identify critical predictor variables such as road network and land-use characteristics that influence pedestrian safety at intersections near light rail transit (LRT) stations, and, (2) to examine the change in pedestrian crash patterns at these intersections before and after the LRT is in operation to serve the users. Pedestrian crashes, road network, and land-use characteristics within a vicinity of 0.25 miles (402 m) at 70 selected intersections near fifteen LRT stations in Charlotte, North Carolina were considered in this research. The predictor variables were examined to minimize multicollinearity and develop four different non-linear regression models. The findings from the three best models indicate that the number of bus stops, mixed use area, office area, single-family residential area, industrial area, and the presence of a railroad flasher have a statistically significant influence on the number of pedestrian crashes at an intersection near an LRT station. An increase in the total number of pedestrian crashes at the selected intersections near LRT stations was observed during the after-period compared to the before-period. The increase in the number of pedestrian crashes varied with the pedestrian crash history of the intersection.

Drivers experience variations in travel time due to congestion and delays associated with road construction projects on existing transportation facilities. The effect on the travel time of a link decreases as its distance from the road construction project increases. In addition, the effect could extend to the connecting arterial street links within the vicinity of the road construction project. Therefore, the focus of this research is to model the effect of a road construction project on the freeway and connecting arterial street link-level travel times. Data pertaining to a resurfacing construction project, which lasted for six months, was gathered from the traffic incident management systems (TIMS). The travel time data was collected and processed, for each selected freeway and connecting arterial street link, for six months before the start of the resurfacing construction project and six months during the construction of the resurfacing project. Generalized linear models (GLM) were developed and validated for before and during the construction project periods with the average travel time on a link as the dependent variable. Data such as time-of-the-day, day-of-the-week, the distance of a link from the work zone, subject link characteristics, and upstream and downstream link characteristics were collected and used as the predictor variables. The results indicate that the subject link, upstream link and downstream link characteristics have a significant effect on the freeway and connecting arterial street link-level travel times due to the resurfacing construction project.

Pedestrian safety is generally assessed using frequency of crashes, based on historical data, for a given transportation facility. However, the lack of good and reliable crash data has hampered its apposite analyses and in evaluating the effectiveness of pedestrian safety programs. To overcome this gap, traffic conflict technique (TCT) which relies mainly on the observations of critical traffic situations for safety analysis were developed. However, the applicability of TCTs and related measures under varying non-lane based heterogeneous traffic conditions prevailing in countries such as India is not widely explored. This paper attempts to evaluate pedestrian safety at urban midblock crosswalk using different surrogate safety measures, including vehicle crossing speed, post encroachment time (PET), yielding compliance of driver as well as pedestrian, and conflict rate. The number of conflicts were observed to increase as the average vehicle crossing speed increases, indicating that pedestrians are extremely vulnerable while crossing the road. The PET value for the smaller vehicles, such as two-wheelers and three-wheelers, is recorded to be lower than the heavy vehicles, such as trucks and buses. With the addition of one lane, there is a significant decrease in the PET value. The average PET values for the vehicle on eight-lane divided road is less than the six-lane divided, four-lane divided, and two-lane undivided roads. Further, the yielding compliance of the driver as well as of the pedestrian depends on the crossing speed of the approaching vehicle and the type of road geometry. Further, the rate and severity of conflicts increased with a decrease in the pedestrian crossing speed. The yielding behaviour of the drivers as well as the pedestrian's yielding compliance varies by location, highlighting the effect of individual and demographic characteristics on pedestrian crossing behaviour.

This research explores correlations between travel time-based measures, comprehensively, by year, day-of-the-week (DoW), time-of-the-day (ToD), week-of-the-year (WoY), and the posted speed limit using raw one-minute interval travel time data for 3,290 road links in Charlotte, North Carolina. The data were processed, normalized, and categorized into 57 datasets accounting for the year, DoW, ToD, WoY, and the posted speed limit. Pearson correlation coefficient matrices were generated to explore the correlations between travel time measures (minimum, maximum, and average; 10th, 15th, 50th, 85th, 90th, and 95th percentile travel times), travel time reliability measures (buffer time – BT, buffer time index – BTI, planning time index – PTI, and travel time index – TTI), and travel time variability measures (based on 90th, 85th, 15th and 10th percentile travel times). The percentages based on scores associated with the correlations were also examined. Travel time measures and travel time variability measures are moderately or highly correlated with each other. BT is moderately or highly correlated with all other measures, while the correlation between BTI, PTI and TTI and travel time measures is relatively low. There is a positive correlation between BTI and PTI and a negative correlation between BTI and TTI in most instances.

The objective of this paper is to evaluate drivers’ risk perception toward crash related traffic rule violations and identify violations that are perceived as low risk to better educate drivers. Risk perceptions on crash related traffic rule violations was gathered from 3,593 participants as a part of Naturalistic Driving Study. The variations in risk perceptions by driver characteristics such as age, gender, education, and household income were studied. The risk perception of violating traffic rules was observed to increase with an increase in drivers’ age, except for driving under the influence of alcohol and drugs. Drivers older than 25 years perceive disregarding traffic signals as the riskiest traffic rule violation. Exceeding speed limit by 10 to 20 mph is perceived as the least risky among the considered traffic rule violations, irrespective of age, gender, education, and income level of the driver. The risk perception of disregarding traffic signals and following vehicle closely are statistically the same for both male drivers and female drivers. For all other traffic rule violations, female drivers’ risk perception is greater than male drivers’ risk perception. Participants with lower education level perceive violating traffic rules as not risky, except for DUI. Graduates or professionals with no advanced degree perceive risk of violating traffic rules greater than the average risk for the entire sample population. Dissemination of risk perception information as well as enhanced educational programs are necessary to increase awareness about the risk associated with violating traffic rules that are perceived as low risk by drivers.

A travel time reliability-based approach is proposed to assess the effect of the light rail transit (LRT) system on the road network within its vicinity. A 4-mile stretch of the Blue Line LRT extension, which connects the Old Concord Road and the University of North Carolina at Charlotte (UNC Charlotte) main campus in Charlotte, North Carolina (NC), was considered as the study corridor. The raw travel time data was collected from the Regional Integrated Transportation Information System (RITIS) website at one-minute intervals. The average travel time (ATT), planning time (PT), buffer time (BT), buffer time index (BTI), and planning time index (PTI) were computed for each link, referred to as Traffic Message Channel (TMC) in this research, by day-of-the-week and time-of-the day. Further, the travel time reliability of the links on the LRT extension corridor and adjacent corridors (both the parallel route and the cross-streets) were computed for different scenarios: network without LRT, testing phase of LRT, first month of LRT operation, third month of LRT operation, sixth month of LRT operation, and ninth month of LRT operation. The travel time reliability of the alternate route and cross-streets was affected by the LRT system operation. Increased green times and better coordination on the LRT corridor and the benefits associated with the alternate mode/route choice for commuters may be the reason behind the steadiness in travel time performance measures due to the LRT. The methodology and findings help transportation planners and engineers in comparing the performance or efficiency of large-scale public transportation projects like LRT and bus rapid transit (BRT) on travel time reliability within its vicinity.

The assessment of operational performance is a challenge for the state, regional and local transportation agencies due to the complex and dynamic nature of traffic movement. A qualitative classification of operational performance is often done in the form of a six letter-grade, A to –F level-of-service (LOS) scale, defined in the Highway Capacity Manual (HCM). It is based on density for basic freeway sections, weaving sections, and merging/diverging areas. Presently, speed and travel time are two performance measures used by the practitioners when making decisions. Further, travel time reliability is becoming an important concept for modern and urban transportation system managers, planners and engineers. Efforts have been initiated through Strategic Highway Research Program (SHRP 2) to quantify LOS based on travel time reliability. To contribute to the body of knowledge, this paper adopts a microscopic simulation based method, and researches the validity of travel time and travel time reliability indices to quantify LOS thresholds for basic freeway sections. Findings indicate that the average travel time per mile threshold values for respective LOS letter-grades increase as the speed limit decreases, until the saturation point where the speed limit on the freeway does not have any influence on the operational performance. As the posted speed limit decreases, the percent difference between two adjacent travel time per mile threshold values also decreases. Further, the average travel time reliability LOS threshold values for the respective LOS letter-grades decrease as the speed limit decreases.

Traffic related crashes cause more than 38,000 fatalities every year in the United States. They are the leading cause of death among drivers up to 54 years in age and incur $871 million in losses each year. Driver errors contribute to about 94% of these crashes. In response, automotive companies have been developing vehicles with advanced driver assistance systems (ADAS) that aid in various driving tasks. These features are aimed at enhancing safety by either warning drivers of a potential hazard or picking up certain driving maneuvers like maintaining the lane. These features are already part of vehicles with Driver Assistance Technology, and they are vital for successful deployment of connected and automated vehicles in the near future. However, drivers' responses to driving vehicles with advanced features have been meagerly explored. This research evaluates driver participants' response to scenarios when driving connected and automated vehicles compared to vehicles with and without Driver Assistance Technology. The research developed rural, urban, and freeway driving scenarios in a driver simulator and tested on participants sixteen years to sixty-five years old. The research team explored two types of advanced features by categorizing them into warnings and automated features. The results show that the advanced features affected driving behavior by making driver participants less aggressive and harmonizing the driving environment. This research also discovered that the type of driving scenario influences the effect of advanced features on driver behavior. Additionally, aggressive driving behavior was observed most in male participants and during nighttime conditions. Rainy conditions and female participants were associated with less aggressive driving behavior. The findings from this research help to assess driver behavior when driving vehicles with advanced features. They can be inputted into microsimulation software to model the effect of vehicles with advanced features on the performance of transportation systems, advancing technology that could eventually save millions of dollars and thousands of lives.

Motor vehicle crashes are one of the leading causes of teen deaths worldwide. It is important to assess the environment and identify the risk factors influencing teen crashes for planning strategies and improving their safety. This research, therefore, focuses on exploring the effect of road network, demographic, and land use characteristics to compute teen crash frequency. Data for 201 spatially distributed road segments in Mecklenburg County, North Carolina, USA were considered for the evaluation. Data related to teen crashes were obtained from the Highway Safety Information System (HSIS). Demographic and land use characteristics were extracted around each selected road segment using two different buffer widths (0.25 miles and 0.5 miles). Teen crash frequency of each road segment was used as the dependent variable. The generalized linear models with the negative binomial distribution (GLM-based NB model) and geographically weighted negative binomial regression models (GWNBR and GWNBRg) were developed and compared. The annual average daily traffic (AADT), light commercial land use, number of household units, and number of pupils enrolled in public or private high schools are significant explanatory variables influencing teen crash frequency. Both methods have good predictive capabilities and can be used to compute teen crash frequency. However, the GWNBR and GWNBRg better capture the spatial dependency and spatial heterogeneity of associated risk factors influencing teen crash frequency.

Efforts were initiated through the Strategic Highway Research Program (SHRP) to quantify level of service (LOS) based on travel time and travel time reliability. To contribute to this goal,...

Trucks serve significant amount of freight tonnage and are more susceptible to complex interactions with other vehicles in a traffic stream. While traffic congestion continues to be a significant ‘highway’ problem, delays in truck travel result in loss of revenue to the trucking companies. There is a significant research on the traffic congestion mitigation, but a very few studies focused on data exclusive to trucks. This research is aimed at a regional-level analysis of truck travel time data to identify roads for improving mobility and reducing congestion for truck traffic. The objectives of the research are to compute and evaluate the truck travel time performance measures (by time of the day and day of the week) and use selected truck travel time performance measures to examine their correlation with on-network and off-network characteristics. Truck travel time data for the year 2019 were obtained and processed at the link level for Mecklenburg County, Wake County, and Buncombe County, NC. Various truck travel time performance measures were computed by time of the day and day of the week. Pearson correlation coefficient analysis was performed to select the average travel time (ATT), planning time index (PTI), travel time index (TTI), and buffer time index (BTI) for further analysis. On-network characteristics such as the speed limit, reference speed, annual average daily traffic (AADT), and the number of through lanes were extracted for each link. Similarly, off-network characteristics such as land use and demographic data in the near vicinity of each selected link were captured using 0.25 miles and 0.50 miles as buffer widths. The relationships between the selected truck travel time performance measures and on-network and off-network characteristics were then analyzed using Pearson correlation coefficient analysis. The results indicate that urban areas, high-volume roads, and principal arterial roads are positively correlated with the truck travel time performance measures. Further, the presence of agricultural, light commercial, heavy commercial, light industrial, single-family residential, multi-family residential, office, transportation, and medical land uses increase the truck travel time performance measures (decrease the operational performance). The methodological approach and findings can be used in identifying potential areas to serve as truck priority zones and for planning decentralized delivery locations.

Collecting traffic data and/or estimating and reporting annual average daily traffic (AADT) is important for planning, designing, building, and maintaining the road infrastructure. However,...

This research is carried out to evaluate the efficacy of toll roads in reducing travel time and improving travel time reliability on links within its vicinity, using data for the Triangle Expresswa...

This paper focuses on forecasting short-term spatiotemporal relative change in travel time (RCTT) on a freeway corridor. The RCTT was used instead of the travel time to capture the relative effect (say) due to a vehicle accident when compared with the no incident condition. Two types of RCTT were considered: travel time/expected travel time and travel time/minimum travel time. Databases were developed using data for 135 “Vehicle Accident” affected days and 128 days when there were no incidents, on a freeway corridor. The expected travel time was computed through averaging of all the travel time samples for a specific time period. The percent difference between the expected travel time and the observed travel time is less than 15% for 90% of the samples, indicating that the computed expected travel time represents the travel time during no incident condition. Autoregressive Integrated Moving Average (ARIMA) model was then applied to model RCTT. The Mean Absolute Percent Error (MAPE) of the fitted models, as well as for the validated models, was less than 15% for most of the segments, indicating the effectiveness of ARIMA in forecasting short-term spatiotemporal RCTT due to a vehicle accident.

This paper focuses on identifying crash risk factors associated with injury severity of teen drivers. Crash data obtained from the Highway Safety and Information System (HSIS) for the entire state of North Carolina, for years 2011 to 2013, was used for analysis and modeling. Among all the crashes during the study period, a total of 62,990 crashes involving teen drivers (15 to 19 years) were analyzed. A partial proportionality odds model was developed to identify factors contributing to injury severity of teen drivers. The results obtained indicate that teen drivers driving sports utility vehicles and pickup trucks are more likely to be severely injured when compared to teen drivers driving passenger cars. Teen drivers are more likely to be severely injured on weekdays, particularly during peak hours. The chances of teen drivers getting involved in severe injury crashes on Tuesdays and Fridays is higher when compared to Sundays. Age, gender, road configuration, terrain, adverse weather condition, and access control are observed to have a significant effect on teen driver's injury severity. Keywords: Teen drivers, Crash, Injury severity, Contributing factor, Partial proportionality odds model

This paper provides insights pertaining to the validity of a regional travel demand model in mimicking real-world travel times. The estimated travel times from the regional travel demand model, for the base year 2015, for Mecklenburg County in the city of Charlotte, North Carolina (NC) were compared with travel time statistics from a private data source, for the same year. The results indicate that the estimated travel times from the regional travel demand model are typically lower than the 85th percentile travel times, irrespective of the link speed limit. The estimated travel times for the Central Business District (CBD) area type are moderately correlated with the travel time statistics from the private data source, irrespective of the time of the day. For all the other area types, stronger correlations were observed when the estimated travel times from the regional travel demand model are compared with 10th to 50th percentile travel times. The calculated Pearson correlation coefficients are low for morning and evening peak periods compared to mid-day and night-time period, indicating the inability of the regional travel demand model in mimicking congested traffic conditions accurately.

The Federal Railroad Administration (FRA)’s Web Based Accident Prediction System (WBAPS) is used by federal, state and local agencies to get a preliminary idea on safety at a rail-highway grade crossing. It is an interactive and user-friendly tool used to make funding decisions. WBAPS is almost three decades old and involves a three-step approach making it difficult to interpret the contribution of the variables included in the model. It also does not directly account for regional/local developments and technological advancements pertaining to signals and signs implemented at rail-highway grade crossings. Further, characteristics of a rail-highway grade crossing vary by track class which is not explicitly considered by WBAPS. This research, therefore, examines and develops a method and models to estimate crashes at rail-highway grade crossings by track class using regional/local level data. The method and models developed for each track class as well as considering all track classes together are based on data for the state of North Carolina. Linear, as well as count models based on Poisson and Negative Binomial (NB) distributions, was tested for applicability. Negative binomial models were found to be the best fit for the data used in this research. Models for each track class have better goodness of fit statistics compared to the model considering data for all track classes together. This is primarily because traffic, design, and operational characteristics at rail-highway grade crossings are different for each track class. The findings from statistical models in this research are supported by model validation.

Examining the relationship between congestion and travel time reliability (TTR) can help in gaining a broader perspective on the development of congestion mitigation strategies. Several per...

The rapid growth in population and related demand for travel during the past few decades has had a catalytic effect on traffic congestion, air quality, and safety in many urban areas. Transportation managers and planners have planned for new facilities to cater to the needs of users of alternative modes of transportation (e.g., public transportation, walking, and bicycling) over the next decade. However, there are no widely accepted methods, nor there is enough evidence to justify whether such plans are instrumental in improving mobility of the transportation system. Therefore, this project researches the operational performance of urban roads with heterogeneous traffic conditions to improve the mobility and reliability of people and goods. A 4-mile stretch of the Blue Line light rail transit (LRT) extension, which connects Old Concord Rd and the University of North Carolina at Charlotte’s main campus on N Tryon St in Charlotte, North Carolina, was considered for travel time reliability analysis. The influence of crosswalks, sidewalks, trails, greenways, on-street bicycle lanes, bus/LRT routes and stops/stations, and street network characteristics on travel time reliability were comprehensively considered from a multimodal perspective. Likewise, a 2.5-mile-long section of the Blue Line LRT extension, which connects University City Blvd and Mallard Creek Church Rd on N Tryon St in Charlotte, North Carolina, was considered for simulation-based operational analysis. Vissim traffic simulation software was used to compute and compare delay, queue length, and maximum queue length at nine intersections to evaluate the influence of vehicles, LRT, pedestrians, and bicyclists, individually and/or combined. The statistical significance of variations in travel time reliability were particularly less in the case of links on N Tryon St with the Blue Line LRT extension. However, a decrease in travel time reliability on some links was observed on the parallel route (I-85) and cross-streets. While a decrease in vehicle delay on northbound and southbound approaches of N Tryon St was observed in most cases after the LRT is in operation, the cross-streets of N Tryon St incurred a relatively higher increase in delay after the LRT is in operation. The current pedestrian and bicycling activity levels seemed insignificant to have an influence on vehicle delay at intersections. The methodological approaches from this research can be used to assess the performance of a transportation facility and identify remedial solutions from a multimodal perspective.

Pedestrian crash data are not available in sufficiently large quantity and suffer from known problems such as a low-mean small sample, underreporting, and misclassification. Moreover, under...

The focus of this research is to investigate the influence of land use and network characteristics within the proximal area of a road link on travel time. Data for 259 links within the cit...

In recent times, amplified pedestrian-vehicle interaction, especially at urban midblock crossings, have ensued in higher risks and fatalities during pedestrian crossings. This situation fur...

Automotive companies have been developing vehicles with advanced features that aid in various driving tasks. These features are aimed at enhancing safety by either warning the drivers of a potential hazard or picking up certain driving maneuvers like maintaining the lane or a constant headway. They are part of vehicles with driver assistance technology and are vital for the successful deployment of connected and automated vehicles in the future. However, drivers’ behavioral response when driving vehicles with such advanced driver assistance systems (ADAS) compared to vehicles without ADAS may vary and is meagerly explored. This research evaluates drivers’ behavioral response to scenarios when driving vehicles with ADAS like lane departure warning (LDW), blind spot warning (BSW), and over speed warning (OSW) compared to vehicles without ADAS. Rural, urban and freeway driving scenarios were developed in a driver simulator and tested on 43 participants aged sixteen years to sixty-five years. The results show that ADAS influence driving behavior by making drivers less aggressive and harmonizing the driving environment. The influence of ADAS on the driver behavior was different in rural, urban, and freeway driving scenarios. The drivers’ behavioral response to scenarios varied with the lighting and weather condition as well as with the age, gender, and ethnicity of the participant. While ADAS help by reducing lane departures or speeding and enhance safety, an indirect influence on braking, turning, and car-following were also observed. The findings help assess driver behavior when driving vehicles with advanced features and build better systems.

The focus of this research paper is on extraction of predictor variables pertaining to on-network, traffic, signal, demographic, and land use characteristics, by area type, and examining their influence on the number of red light violation crashes. Data for the city of Charlotte, North Carolina was extracted and used for analysis. Three different sets of signalized intersections were selected in the three different area types - Central Business District (CBD), urban, and suburban areas. Each set is comprised of sixty signalized intersections (total 180 signalized intersections). The number of red light violation crashes from January 2010 to December 2014, within the vicinity of each selected signalized intersection, was considered as the dependent variable to develop crash estimation models for each area type. The crash estimation models by area type were compared with the crash estimation model developed considering all the 180 signalized intersections together. Different predictor variables were found to be significant at a 95% confidence level in three different areas. Log-link model with Negative Binomial distribution was observed to best fit the data used in this research. Findings indicate that enforcement, either manually or using red light running cameras (RLCs), at signalized intersections with high traffic volume in the CBD area; at signalized intersections with high traffic volume, high all-red clearance time, near high density of horizontal mixed non-residential and open space/recreational type land uses in urban area; at signalized intersections with high traffic volume, speed limit on the major approach, the number of lanes on the minor approach, and all-red clearance time and areas surrounded with horizontal mixed non-residential and retail type land use in suburban areas, would lead to a reduction in the number of red light violation crashes.

The focus of this paper is on an evaluation of negative consequences due to the installation of pedestrian hybrid beacon (PHB) signals and to identify associated characteristics to proactively plan and reduce danger at mid-block locations. Data for thirteen mid-block locations with a PHB signal in the city of Charlotte, North Carolina was gathered to conduct before-after analysis using Empirical Bayes (EB) method. Data for fifty-nine mid-block locations without a PHB signal or similar treatment was considered as control locations for developing a safety performance function (SPF). The on-network characteristics were considered as the predictor variables, while the number of all crashes from 2011 to 2013 was considered as the dependent variable. The SPF was validated using 2014 data. A calibration factor was applied to estimate the number of all crashes at each PHB location. The results obtained from before-after EB analysis showed that the actual number of all crashes are relatively higher after the installation of PHB signals at five out of the thirteen PHB signal locations when compared with the estimated number of all crashes, had the PHB signal not been installed. Pearson correlation coefficients were estimated and used to examine the relationship between the number of all crashes and predictor variables such as demographic, socio-economic, land use, and on-network characteristics within the vicinity of each PHB signal location. The findings indicate that installing PHB signals at mid-block locations along high traffic volume, high-speed and wide roads, and, near office, multi-family, retail, and vertical mixed land use areas will lead to an increase in the number of all crashes after its installation.

This paper focuses on quantifying energy loss associated with recurring and nonrecurring congestion components and understanding its spatio-temporal variations in a geospatial environment. Data gat...