Srinivas S. Pulugurtha

Pedestrian crash estimation models for signalized intersections

The focus of this paper is twofold: (1) to examine the non-linear relationship between pedestrian crashes and predictor variables such as demographic characteristics (population and household units), socio-economic characteristics (mean income and total employment), land use characteristics, road network characteristics (the number of lanes, speed limit, presence of median, and pedestrian and vehicular volume) and accessibility to public transit systems, and (2) to develop generalized linear pedestrian crash estimation models (based on negative binomial distribution to accommodate for over-dispersion of data) by the level of pedestrian activity and spatial proximity to extract site specific data at signalized intersections. Data for 176 randomly selected signalized intersections in the City of Charlotte, North Carolina were used to examine the non-linear relationships and develop pedestrian crash estimation models. The average number of pedestrian crashes per year within 200 feet of each intersection was considered as the dependent variable whereas the demographic characteristics, socio-economic characteristics, land use characteristics, road network characteristics and the number of transit stops were considered as the predictor variables. The Pearson correlation coefficient was used to eliminate predictor variables that were correlated to each other. Models were then developed separately for all signalized intersections, high pedestrian activity signalized intersections and low pedestrian activity signalized intersections. The use of 0.25mile, 0.5mile and 1mile buffer widths to extract data and develop models was also evaluated.

Assessment of Models to Measure Pedestrian Activity at Signalized Intersections

The allocation of resources to build facilities amicable to pedestrians is governed by pedestrian activity at the location of interest. Collecting real-world data such as pedestrian counts at each point of interest is an expensive and time-consuming process. However, unlike trip generation models to estimate vehicle trips, the literature documents limited research to model and measure activity pertaining to pedestrian counts. The development and an assessment of models to measure pedestrian activity at signalized intersections are presented. Data collected at 176 signalized intersections in the city of Charlotte, North Carolina, are used to develop models to measure pedestrian activity by the time of day at signalized intersections. Pedestrian counts collected at the 176 intersections are used as a dependent variable. Factors such as demographic characteristics (population, household units), socioeconomic characteristics (income level, employment), land use characteristics (residential, commercial, industrial, etc.), network characteristics (number of approaches, number of lanes, speed limits, traffic volume, presence of medians), and the number of transit stops are extracted and estimated by using features available in a commercial geographic information system software program. These factors are used as independent variables. Multiple regression analysis through backward elimination of independent variables is used to develop the models. The developed models could be used by practitioners to measure pedestrian activity at a location if data are available. The measured pedestrian activity could be used in transportation planning, safety, and operational analyses.

Traffic analysis zone level crash estimation models based on land use characteristics

The objective of this paper is to develop crash estimation models at traffic analysis zone (TAZ) level as a function of land use characteristics. Crash data and land use data for the City of Charlotte, Mecklenburg County, North Carolina were used to illustrate the development of TAZ level crash estimation models. Negative binomial count models (with log-link) were developed as data was observed to be over-dispersed. Demographic/socio-economic characteristics such as population, the number of household units and employment, traffic indicators such as trip productions and attractions, and, on-network characteristics such as center-lane miles by speed limit were observed to be correlated to land use characteristics, and, hence were not considered in the development of TAZ level crash estimation models. Urban residential commercial, rural district and mixed use district land use variables were observed to be correlated to other land use variables and were also not considered in the development of the models. Results obtained indicate that land use characteristics such as mixed use development, urban residential, single-family residential, multi-family residential, business and, office district are strongly associated and play a statistically significant role in estimating TAZ level crashes. The coefficient for single-family residential area was observed to be negative, indicating a decrease in the number of crashes with an increase in single-family residential area. Models were also developed to estimate these crashes by severity (injury and property damage only crashes). The outcomes can be used in safety conscious planning, land use decisions, long range transportation plans, and, to proactively apply safety treatments in high risk TAZs.

Are pedestrian countdown signals effective in reducing crashes

Objective: The time left to cross the street displayed on pedestrian countdown signals can be used by pedestrians as well as drivers of vehicles, though these signals are primarily provided to help pedestrians make better crossing decisions at signalized intersections. This article presents an evaluation of the effect of pedestrian countdown signals in reducing vehicle–pedestrian crashes and all crashes at signalized intersections. Methods: A before-and-after study approach was adopted to evaluate the effect considering pedestrian countdown signals installed over a 5-month period at 106 signalized intersections in the city of Charlotte, North Carolina. Results: Analysis conducted at 95 percent confidence level showed that there has been a statistically insignificant decrease in vehicle–pedestrian crashes but a statistically significant decrease in all (includes vehicle–pedestrian and vehicle(s) only involved) crashes after the installation of pedestrian countdown signals. No negative consequences were observed after the installation of pedestrian countdown signals. Sixty-eight percent of the signalized intersections saw a decrease in the total number of all crashes, and 4 percent of the signalized intersections have not seen any change in the number of all crashes after the installation of pedestrian countdown signals. Improvements in terms of decrease in the total number of all crashes was high at signalized intersections with greater than 10 crashes per year during the before period. Likewise, decrease in the number of all crashes was high at signalized intersections with traffic volume between 7 AM to 7 PM greater than 20,000 vehicles during the before period. Conclusions: Based on results obtained, it can be concluded that pedestrians as well as drivers are making better decisions using the time left to cross the street displayed on pedestrian countdown signals at signalized intersections in the city of Charlotte, North Carolina.

Evaluating the role of weaving section characteristics and traffic on crashes in weaving areas.

Objective: The likelihood of being involved in a crash on a freeway, in general, is greater on weaving sections than on basic freeway sections and in ramp influence areas. This is due to possible crossing of entry and/or exit traffic over a short distance while traveling in the same direction without the aid of traffic control devices resulting in potential conflicting situations and crashes. This article focuses on evaluating the role of weaving section characteristics (configuration type, length and the number of required lane changes by weaving traffic) and traffic variables (entry volume, exit volume, and non-weaving volume) on crashes in weaving areas. Methods: Data collected for 25 weaving sections in the Las Vegas metropolitan area are used to study the relationship between crashes and weaving section characteristics and traffic variables. The relationship between (1) crashes by selected collision types and contributing factors and (2) weaving section characteristics and traffic variables are also examined. Descriptive and statistical analysis techniques were used. Results: The number of crashes tends to decrease with increase in length of weaving section. Increase in entry volume increases crashes due to improper lane change and ran off roadway crashes. On the other hand, increase in exit volume increases rear-end crashes, crashes due to following too closely, and crashes due to inattentive driving. Non-weaving volume, in general, also appears to play a prominent role in explaining most crash types and contributing factors on weaving sections. Conclusions: Type “A” weaving section tends to be relatively safer when compared to other weaving configuration section types. Retrofitting weaving sections with short lengths or designing sections with longer lengths, in addition to increased use of in-vehicle warning systems, may reduce crashes and improve safety. Ramp metering, enforcement, changeable message signs (speed signs), and capacity improvements are other solutions for consideration.

Principle of Demographic Gravitation to Estimate Annual Average Daily Traffic: Comparison of Statistical and Neural Network Models

This paper focuses on the application of the principle of demographic gravitation to estimate link-level annual average daily traffic (AADT) based on land-use characteristics. According to the principle, the effect of a variable on AADT of a link decreases with an increase in distance from the link. The spatial variations in land-use characteristics were captured and integrated for each study link using the principle of demographic gravitation. The captured land-use characteristics and on-network characteristics were used as independent variables. Traffic count data available from the permanent count stations in the city of Charlotte, North Carolina, were used as the dependent variable to develop statistical and neural network models. Negative binomial count statistical models (with log-link) were developed as data were observed to be over-dispersed while neural network models were developed based on a multilayered, feed-forward, back-propagation design for supervised learning. The results obtained indicate that statistical and neural network models ensured significantly lower errors when compared to outputs from traditional four-step method used by regional modelers. Overall, the neural network model yielded better results in estimating AADT than any other approach considered in this research. The neural network approach can be particularly suitable for their better predictive capability, whereas the statistical models could be used for mathematical formulation or understanding the role of explanatory variables in estimating AADT.

Assessment of Models to Estimate Bus-Stop Level Transit Ridership using Spatial Modeling Methods

The objective of this research is to develop and assess bus transit ridership models at a bus-stop level using two spatial modeling methods: spatial proximity method (SPM) and spatial weight method (SWM). Data for the Charlotte (North Carolina) area are used to illustrate 1) the working of the methods and 2) development and assessment of the models. Features available in Geographic Information System (GIS) software were explored to capture spatial attributes such as demographic, socioeconomic, and land use characteristics around each selected bus stop. These, along with on-network characteristics surrounding the bus stop, were used as explanatory variables. Models were then developed, using the generalized estimating equations (GEE) framework, to estimate riders boarding (dependent variable) at the bus stop as a function of selected explanatory variables that are not correlated to each other. Results obtained indicate that Negative Binomial with log-link distribution better fits the data to estimate ridership at the bus-stop level (for both SPM and SWM) than when compared to linear, Poisson with log-link and Gamma with log-link distributions. Although SPM models demonstrated distance decay behavior, statistical parameters indicate that SWM (based on functions 1/D, 1/D2, and 1/D3) does not yield better or more meaningful estimates than when compared to SPM using 0.25-mile buffer width data.

Assessment of Link Reliability as a Function of Congestion Components

Travelers’ perception of reliability of a road network is typically based on factors contributing to both recurring and nonrecurring congestion. However, literature documents little research to integrate, estimate and assess congestion using both these disparate congestion components. Integrating recurring and nonrecurring congestion components to estimate congestion helps address questions such as “How reliable are roads in the transportation network?” “Which path is more reliable to reach the destination from an origin within an on-time window during a certain time of the day?” The focus of this paper is to develop and illustrate the working of a methodology to estimate travel time and its variations, travel delay index due to crashes and their severity, congestion score and reliability of each link in the network. Traffic volume, link capacity, travel speed, crashes and their severity, and estimated time taken for normal traffic conditions to restore after a crash are used in the computations. Temporal...

Hazardous Bus Stop Identification: An Illustration Using GIS

Safety and accessibility to bus transit systems play a vital role in increasing transit market potential. Bus passengers often tend to cross the streets from either behind or in front of the bus as crosswalks do not exist near most bus stops, which are typically away from intersections. These unsafe maneuvers frequently result in either auto-pedestrian collisions or conflicts. Identifying hazardous bus stops would serve as a building block to study the causal factors, select mitigation strategies, and allocate safety funds to improve bus passenger safety. The focus of this article is to develop a Geographic Information Systems (GIS) based methodology to assist decisionmakers in identifying and ranking bus stops in high auto-pedestrian collision concentration areas. The working of the GIS-based methodology is illustrated using 2000–2002 auto-pedestrian collision data, traffic volumes, bus stop coverage, transit ridership data, and street centerline coverage for the Las Vegas Metro area. Results obtained are sensitive to buffer radius and ranking methods used to rank hazardous bus stops. Potential strategies and countermeasures to enhance safety at hazardous bus stops are also discussed.

Estimating Pedestrian Counts in Urban Areas for Transportation Planning and Safety Analyses

This paper investigates factors that can be used to quantify pedestrian counts in urban areas. The best subset regression is used to develop models to estimate pedestrian counts considering variables identified using general linear regression. The F-test is used to support the analysis. The models are developed using data collected at 15 selected locations with high pedestrian activity in the Las Vegas Metropolitan area. The findings show that the pedestrian counts are a function of the number of lanes, average annual household income and residential area proximity to the study location. Results show that the pedestrian counts are independent of the commercial area and the number of bus stops in the vicinity of the location. The developed models can be used to estimate pedestrian counts at any high pedestrian activity location provided the socioeconomic the demographic characteristics are known. The methodology is also applicable to other urban settings.