Janice Daniel

Estimation of Bus Dwell Times with Automatic Passenger Counter Information

The average passenger boarding and alighting times and bus dwell times at stops are important information for estimating transit service capacities. Bus dwell time directly affects vehicle travel time, and thus the fleet size required to provide service based on scheduled headway is affected. Research focused on estimating bus dwell time and the impact of boarding and alighting passengers on dwell time. In addition, the effect of standees, time of day, and service type on bus dwell time was investigated. The data were recently collected from an archived database, within which automatic passenger counter information was recorded. The dwell times and passenger counts were recorded daily during 2001 and the first 6 months of 2002. The bus dwell time and average passenger boarding and alighting time at stops are explained using descriptive statistics.

Analysis of Fatal Crashes in Georgia Work Zones

Studies on work zone crashes typically examine a combination of injury, fatal, and property damage crashes to identify factors that contribute to unsafe conditions within work zones. Few studies focus on fatal crashes within work zones, although a clear understanding of the driver, roadway, and work zone conditions associated with fatal crashes will facilitate the development of strategies aimed at improving safety and reducing fatal as well as nonfatal crashes. The Georgia Department of Transportation recently performed a study to identify the manner of collision, location, and construction activity associated with fatal crashes in work zones. This study is expanded further to examine the difference between fatal crash activity within work zones compared with fatal crashes in non-work-zone locations. Using data from three work zone locations in Georgia, fatal crash activity within work zones also was compared with nonfatal crashes within work zones. Finally, fatal crash activity was examined to determine the influence of the work zone activity on the frequency of fatal crashes. The overall findings of the study indicate that the work zone influences the manner of collision, light conditions, truck involvement, and roadway functional classification under which fatal crashes occur. The study also indicates that fatal crashes in work zones are more likely to involve another vehicle than non-work-zone fatal crashes, and fatal crashes in work zones are less influenced by horizontal and vertical alignment than are non-work-zone crashes.

Motor Vehicle Driver Injury Severity Study under Various Traffic Control at Highway-Rail Grade Crossings in the United States

INTRODUCTION Based on the Federal Railway Administration (FRA) database, approximately 62% of the collisions at highway-rail crossings occurred at locations with active controls (gate and flashing lights), followed by passive controls (cross bucks and stop signs) with approximately 28% of accidents. METHOD The study applied an ordered probit model to explore the determinants of driver injury severity under various control measures at highway-rail grade crossing in the United States. RESULTS The analysis found that schedule factor (peak hour), visibility, motor vehicle speed, train speed, drivers age, area type, traffic volume and highway pavement impact driver injury severity at both active and passive highway-rail crossings. PRACTICAL APPLICATIONS For both active and passive control highway-rail grade crossings, speed control for both trains and vehicles will significantly reduce driver injury severity. However, the level of influence by vehicle speed and train speed at passive control is higher compared with active control. Paving highways at highway-rail grade crossings will also help to reduce driver injury severity at highway-rail crossing accidents.

Evaluation of Transit Signal Priority and Optimal Signal Timing Plans in Transit and Traffic Operations

Transit signal priority (TSP) systems have been in place for several years. Traffic simulation models are frequently applied to evaluate the benefits of such treatments before implementation. These benefits can take several forms, including reduced travel time for transit customers, improved schedule adherence, and reduced operating costs for the transit provider. This paper reports a case study that highlighted and embodied the following issues: (a) if signal timing is optimized, the TSP may provide only incremental benefits and not be a cost-effective investment; (b) if signal plans are not updated as often as needed, TSP may provide a way of adapting the base signalization, providing much of the benefit of signal plan updates; (c) side-street traffic does not always suffer because of TSP, nor does main-street nonbus traffic; (d) some critical intersections, with heavy competing volumes, may need to be dropped from the TSP plan to have no excessive adverse impact on the cross street, which may itself be an arterial; and (e) bus travel time reductions can result in fewer buses needed to serve the demand at the same level of service and thus reduce bus transit operating costs. Simulation was an important and cost-effective tool in this case study.

Severity of Injuries to Motor Vehicle Drivers at Highway-Rail Grade Crossings in the United States

There are approximately 240,000 highway–rail grade crossings in the United States. High crash frequencies at these locations have led to continued research in safety modeling. Existing crash models for highway–rail grade crossings can be classified into two categories: models for predicting accident frequency and models of the severity of driver injuries. The majority of these studies have focused on the first category. Few studies have focused on the severity of injuries to motor vehicle drivers at highway–rail grade crossings. The objective of this study was to determine the contributing factors that influence the severity of driver injuries in accidents at highway–rail grade crossings. Probit models showed that the following factors were significant: whether the crash occurred during the peak hour, weather, visibility, vehicle type, vehicle speed, annual average daily traffic, train speed, driver age and gender, area type, and type of highway pavement. A marginal effects analysis was also conducted to quantitatively interpret the marginal effects of the contributing factors on each severity level for the highway driver.

FACTORS IN TRUCK CRASHES ON ROADWAYS WITH INTERSECTIONS

Accident prediction models have been used to identify factors that may contribute to crashes at a particular location. Models have been developed separately for highway locations and for intersections. Few accident prediction models have been developed specifically for truck crashes on roadways affected by traffic signals. On arterial roadways with a large number of intersections, it is difficult to isolate the relationship between accidents and specific geometric features, including intersections, because of the proximity of many of these elements to each other. For this reason, an accident prediction model that treats intersections as a geometric feature on the roadway may allow for both the impact of the intersection and of the geometric features of the adjacent roadway segments to be accounted for in one model. By using a database developed for truck accidents in New Jersey for 1998 and 1999, an overall analysis of truck accidents at signalized intersections along US-1 in New Jersey was performed. A Poisson regression and negative binomial model approach was used, and an accident model for estimating accidents on arterial roadways influenced by signalized intersections was determined.

Estimating Free-Flow Speeds for Rural Multilane Highways

The National Highway System Designation Act of 1995 repealed the national maximum speed limit. As a result, many of the states have increased posted speed limits to 105 km/h (65 mph) for select multilane highways. This study evaluates the distribution of the speed and studies the influence of site characteristics, light conditions, and type of vehicle on the field-measured free-flow speed. The suitability of the Highway Capacity Manual multilane highway rules of thumb for freeflow speed to both an 89- and a 105-km/h (55- and 65-mph) posted speed limit condition is also discussed.

Impact of Belt Use by Rear-Seat Occupants on Injury Severity of Belted Drivers in New Jersey:

In January 2010, legislation in New Jersey required all occupants of vehicles to wear seatbelts regardless of seating positions. Although it is known that an unbelted rear-seat passenger affects the safety of the driver, the level of the impact is not clear. The objective of this research was to identify the factors that influenced the injury severity of a belted driver with and without rear-seat occupants. Injury severity models were developed with crash data from the Fatality Analysis Reporting System for New Jersey for 2004 to 2006. The results indicated that the probability that a driver or right-front-seat occupant would sustain more-severe injuries was higher when a left-backseat, middle-backseat, or right-backseat occupant was unbelted. For vehicles with two backseat occupants (left-back and right-back), coefficient values for these variables indicated that lack of seat belt use by either occupant affected the injury severity level of a belted driver. The results showed that the right-backseat occupant had the greatest effect on the injury level of drivers compared with the left-back and middle-back occupants. Although seat belt use by a backseat occupant was found to affect the injury severity of a belted driver, this research demonstrated that the effect became more significant or increased as the number of backseat occupants in the vehicle increased.

GEOGRAPHIC INFORMATION SYSTEM-BASED TRUCK ACCIDENT INFORMATION AND MANAGEMENT SYSTEM FOR NEW JERSEY ROADWAYS

Geographic information systems (GIS) technology has been discussed for managing traffic accident data for years. However, its usefulness for analyzing truck accidents has not been explored extensively. Because GIS has spatial referencing and graphical display features, it would be useful to link relevant accident information to assist in information management and safety analysis. Used with other analysis technologies, GIS can help identify factors that contribute to accidents at specific locations and to identify countermeasures. The New Jersey Department of Transportation (NJDOT) has comprehensive electronic databases, including roadway networks, truck accident records, roadway geometry, and other relevant information, but use of this information remains at the level of database management. Integration of truck accident information into a GIS is a low-cost, high-benefit way to analyze factors that affect truck accidents and their associated mitigation measures. The analytical ability of a GIS-based truck accident information management system developed for NJDOT is presented.

Factors Influencing Seat Belt Usage Rate for Blacks and Hispanics

Seat belt usage among African Americans and Hispanics has been documented to be significantly lower than that of other population groups. In 1996, an NHTSA study showed low seat belt usage rates among the general population and among African Americans especially. Studies have continued to be performed to measure seat belt usage among African Americans and Hispanics. The Division of Highway Traffic Safety of New Jersey commissioned a study to determine seat belt usage rates for blacks and Hispanics in urban areas in New Jersey and to identify differences in these rates and factors that affect the usage rates. Direct observation was used to obtain safety belt usage for drivers in passenger motor vehicles. Race, ethnicity, and socioeconomic data were also obtained through a questionnaire given to drivers of noncommercial motor vehicles. The study found no significant difference between the usage rates of Asians, blacks, Hispanics, and whites. A logistic regression model was also developed to estimate the probability of seat belt use, which was regarded as the dependent variable, with independent variables including gender, vehicle type, racial–ethnic group, highest educational level achieved, marital status, age, whether the driver had children, and total household income. The variables identified as significant included gender, marital status, and age. Race was not found to be a significant factor in estimating the probability of seat belt usage.