Albert Gan

Forecasting Transit Walk Accessibility: Regression Model Alternative to Buffer Method

A description is given of a methodology for estimating transit walk accessibility at the home end of transit trips and for forecasting transit walk accessibility at the home end for a future year, given forecast population and employment data, transit route information, and type of street configuration. The methodology for estimating transit walk accessibility overcomes the problems associated with natural and man-made barriers such as water bodies and community walls and the problem of uneven distribution of population. A comparison of the results with those from the traditional buffer method, as well as with network ratio methods that consider actual walk distance along streets, showed that both the buffer method and network ratio methods tended to overestimate transit walk accessibility. Regression analysis also showed that the new transit walk accessibility measure was a stronger predictor of transit use than that produced using the buffer method. The methodologies may be applied to transit planning, urban design for sustainable development, and long-range transit demand modeling.

Development of Crash Reduction Factors: Methods, Problems, and Research Needs

Crash reduction factors (CRFs) are used in highway safety studies to predict safety benefits due to reduced numbers of crashes. NCHRP Report 162 identified the need for the development of a national comprehensive set of CRFs for each state to evaluate safety improvements. However, this need has not been met. As a result, many states have developed their own CRFs or have adopted CRFs from other states. A synthesis of the CRF development methods, the associated problems, and the research needs are provided. The emphasis is placed on the before-and-after study method because it has been, and still is, the method of choice for CRF development. Three before-and-after study methods were introduced and reviewed: (a) the simple before-and-after method, (b) the before-and-after study with comparison group method, and (c) the before-and-after study with the empirical Bayes method. The problems associated with the simple before-and-after studies, including regression to the mean, crash migration, maturation, and external causal factor, are discussed. Several research needs related to crash migration and general CRF development are also identified. The information presented in the synthesis will be useful to states that plan to develop or update their CRFs.

Identifying Secondary Crashes and Their Contributing Factors

Secondary crashes are generally understood as crashes that occur as a result of primary incidents. Research is needed to determine the nature and cause of these crashes. However, to date, this research has been limited for two reasons. First, there is no uniform definition of secondary crashes. Second, the usually poor quality of incident data and the unavailability of related traffic data make any analysis of secondary crashes difficult. This paper describes a study that uses a comprehensive incident database from District 4 of the Florida Department of Transportation to identify freeway secondary crashes and their contributing factors. A method based on a cumulative arrival and departure traffic delay model was developed to estimate the maximum queue length and the associated queue recovery time for incidents with lane blockages. Descriptive statistics and logistic regression analysis were applied to understand the factors contributing to secondary crashes. The logistic regression analysis indicates that the following four factors have significant effects on the likelihood of secondary crashes: primary incident type, primary incident lane-blockage duration, time of day, and whether the incident occurred on northbound I-95.

Effect of driver’s age and side of impact on crash severity along urban freeways: A mixed logit approach

INTRODUCTION This study identifies geometric, traffic, environmental, vehicle-related, and driver-related predictors of crash injury severity on urban freeways. METHOD The study takes advantage of the mixed logit models ability to account for unobserved effects that are difficult to quantify and may affect the model estimation, such as the drivers reaction at the time of crash. Crashes of 5 years occurring on 89 urban freeway segments throughout the state of Florida in the United States were used. Examples of severity predictors explored include traffic volume, distance of the crash to the nearest ramp, and detailed drivers age, vehicle types, and sides of impact. To show how the parameter estimates could vary, a binary logit model was compared with the mixed logit model. RESULTS It was found that the at-fault drivers age, traffic volume, distance of the crash to the nearest ramp, vehicle type, side of impact, and percentage of trucks significantly influence severity on urban freeways. Additionally, young at-fault drivers were associated with a significant severity risk increase relative to other age groups. It was also observed that some variables in the binary logit model yielded illogic estimates due to ignoring the random variation of the estimation. Since the at-fault drivers age and side of impact were significant random parameters in the mixed logit model, an in-depth investigation was performed. It was noticed that back, left, and right impacts had the highest risk among middle-aged drivers, followed by young drivers, very young drivers, and finally, old and very old drivers. IMPACT ON INDUSTRY To reduce side impacts due to lane changing, two primary strategies can be recommended. The first strategy is to conduct campaigns to convey the hazardous effect of changing lanes at higher speeds. The second is to devise in-vehicle side crash avoidance systems to alert drivers of a potential crash risk. CONCLUSIONS The study provided a promising approach to screening the predictors before fitting the mixed logit model using the random forest technique. Furthermore, potential countermeasures were proposed to reduce the severity of impacts.

SIGNAL TIMING OPTIMIZATION FOR OVERSATURATED NETWORKS USING TRANSYT-7F

Oversaturated traffic networks with spill-back conditions are becoming more common, and their signal timing is difficult to optimize. Up through release 7, TRANSYT-7F, a signal timing tool for traffic network simulation and optimization, was designed for modeling only undersaturated networks. Release 8, which was recently completed, has the added capability to model oversaturated networks. Because signal timing optimization for oversaturated networks requires emphasis on traffic throughput and queue management rather than on traditional delay and progression, four new optimization objective functions were introduced in release 8. This paper introduces these new objective functions and compares their ability to produce optimal timing plans with the abilities of the four traditional objective functions. The types of spill-back effects explicitly modeled by release 8 are also introduced. For performance evaluation, a data set involving two closely spaced ramp terminals at an urban diamond interchange was modeled. The CORSIM microscopic simulation program was used as a tool to evaluate optimal timing designs produced by different objective functions. The results indicate that the new objective functions were able to produce superior timing plans that yield lower average vehicle delay and reduce spill-back conditions.

Analyzing pedestrian crash injury severity at signalized and non-signalized locations

This study identifies and compares the significant factors affecting pedestrian crash injury severity at signalized and unsignalized intersections. The factors explored include geometric predictors (e.g., presence and type of crosswalk and presence of pedestrian refuge area), traffic predictors (e.g., annual average daily traffic (AADT), speed limit, and percentage of trucks), road user variables (e.g., pedestrian age and pedestrian maneuver before crash), environmental predictors (e.g., weather and lighting conditions), and vehicle-related predictors (e.g., vehicle type). The analysis was conducted using the mixed logit model, which allows the parameter estimates to randomly vary across the observations. The study used three years of pedestrian crash data from Florida. Police reports were reviewed in detail to have a better understanding of how each pedestrian crash occurred. Additionally, information that is unavailable in the crash records, such as at-fault road user and pedestrian maneuver, was collected. At signalized intersections, higher AADT, speed limit, and percentage of trucks; very old pedestrians; at-fault pedestrians; rainy weather; and dark lighting condition were associated with higher pedestrian severity risk. For example, a one-percent higher truck percentage increases the probability of severe injuries by 1.37%. A one-mile-per-hour higher speed limit increases the probability of severe injuries by 1.22%. At unsignalized intersections, pedestrian walking along roadway, middle and very old pedestrians, at-fault pedestrians, vans, dark lighting condition, and higher speed limit were associated with higher pedestrian severity risk. On the other hand, standard crosswalks were associated with 1.36% reduction in pedestrian severe injuries. Several countermeasures to reduce pedestrian injury severity are recommended.

Prediction of Lane Clearance Time of Freeway Incidents Using the M5P Tree Algorithm

A number of existing studies have attempted to predict freeway incident duration or incident clearance time. Because lane blockage is the main cause of congestion during freeway incidents, it is more beneficial to predict the lane clearance time instead of the incident clearance time for incidents that involve lane blockages. However, previous studies have not developed prediction models for the lane clearance time. This paper utilizes the M5P tree algorithm for lane clearance time prediction, which has advantages, compared with traditional prediction algorithms. These advantages include the M5P tree algorithms ability to deal with categorical and continuous variables and variables with missing values. The developed model shows that there are a number of variables that affect the lane clearance time, including the number of lanes blocked, time of day, types and number of vehicles involved, the response by the Severe Incident Response Vehicle (SIRV), and traffic management center response and verification times. Comparison results show that the developed model can generally achieve better prediction results than the traditional regression and decision tree models.

Performance of Transit Signal Priority with Queue Jumper Lanes

Queue jumper lanes are a special type of bus preferential treatment that allows buses to bypass a waiting queue through a right-turn bay and then cut out in front of the queue by getting an early green signal. The performance of queue jumper lanes is evaluated under different transit signal priority (TSP) strategies, traffic volumes, bus volumes, dwell times, and bus stop and detector locations. Four TSP strategies are considered: green extension, red truncation, phase skip, and phase insertion. It was found that queue jumper lanes without TSP were ineffective in reducing bus delay. Queue jumper lanes with TSP strategies that include a phase insertion were found to be more effective in reducing bus delay while also improving general vehicle operations than those strategies that do not include this treatment. Nearside bus stops upstream of check-in detectors were preferred for jumper TSP over far-side bus stops and nearside bus stops downstream of check-in detectors. Through vehicles on the bus approach we...

Identifying traditional and nontraditional predictors of crash injury severity on major urban roadways

Objective: This study identifies and compares the factors that contribute to injury severity on urban freeways and arterials and recommends potential countermeasures to enhance the safety of both facilities. The study makes use of an extensive data set from the State of Florida in the United States. To obtain a more complete picture, this study explores both traditional and nontraditional severity predictors. Some traditional predictors include traffic volume, speed limit, and road surface condition. The nontraditional predictors are comprised of those rarely explored in previous severity studies, including crash distance to the nearest ramp location, detailed vehicle types, and lighting and weather conditions. Methods: The analysis was conducted using the ordered and binary probit models, which are well suited for the inherently ordered property of injury severity. Results: An important finding is the significance of the distance of crash to the nearest ramp junction/access point, for which the increase in the distance yielded a severity increase at both facilities. Other significant factors included traffic volume, speed limit, at-fault drivers age, road surface condition, alcohol and drug involvement, and left and right shoulder widths. In comparing both facilities, sport utility vehicles (SUVs) and pickup trucks showed a fatality/severity increase on freeways and a decrease on arterials. Furthermore, the detailed list of variables such as crash time provided pertinent severity trend information that showed that, compared to the other periods, the afternoon peak period had the highest reduction in fatality/severity. Conclusions: Both probit models succeeded in identifying significant severity predictors for each facility. The binary probit model outperformed the ordered probit model based on the higher elasticities (marginal effects) for the fatality/severity probability change, as well as the goodness of fit. As such, this study provides the guidelines for assessing the impact of important roadway and traffic characteristics on crash injury severity along freeways and arterials.

Transit Network Optimization: Minimizing Transfers and Maximizing Service Coverage with an Integrated Simulated Annealing and Tabu Search Method

This paper presents a mathematical methodology for transit route network optimization. The goal is to provide an effective computational tool for optimization of a large-scale transit route network. The objectives are to minimize transfers and maximize service coverage. Formulation of the method consists of three parts: representation of transit route network solution search spaces, representation of transit route and network constraints, and a stochastic search scheme capable of finding the expected global optimal result on the basis of an integrated simulated annealing, tabu, and greedy search algorithm. The methodology has been tested with published solutions to benchmark problems and applied to a large-scale realistic network optimization problem in Miami-Dade County, Florida.