Kevan Shafizadeh

BICYCLE JOURNEY-TO-WORK: TRAVEL BEHAVIOR CHARACTERISTICS AND SPATIAL ATTRIBUTES

The relationship between the demographic attributes and spatial clustering of individuals making a weekday bicycle journey-to-work commute and their commuting travel time is explored. The study uses data from a 1993 bicycle-intercept survey distributed in Seattle, Washington, in which individual bicycle-travel behavior characteristics were collected. The data include socioeconomic information, such as age, gender and income. The results indicate that these three factors may play unexpected roles in the length of bicycle commuting travel times for the journey-to-work trips. This study also suggests that separated bicycle paths play an integral part in the overall bicycle transportation network. Statistical analysis also indicated that cyclists traveling primarily on separated paths tend to make significantly longer trips.

Evaluation of Operation and Accuracy of Available Smart Growth Trip Generation Methodologies for Use in California

It is well known that a standard application of the ITE trip rates for an area with many smart growth characteristics will result in an over-estimation of the number of trips generated. No commonly agreed on methodology in the United States for estimating trip generation takes into account the smart growth characteristics of a land use development project. Several methods have been recently proposed as incremental advancements toward developing such a methodology. This paper identifies eight available methodologies, five of which are appropriate for use in California. The five candidate methods are compared with the traditional ITE trip generation method in a two-part assessment. The first part involves evaluating the methods against a variety of operational criteria developed through discussions with a panel of transportation practitioners. The second part involves testing the accuracy of the methods by comparing the predictions of the various methods against available traffic counts and other data at 22 California sites that contain at least some characteristics of smart growth. On the basis of the evaluation, the authors conclude that all five methods have advantages and disadvantages and that while no single method emerged as the best for use in smart growth development projects, all methods appear to be more accurate at predicting the number of trips generated than standard application of the ITE base rates. Furthermore, this analysis focuses on candidate methods deemed appropriate for use in California, but this research has value and potential implications for smart growth transportation planning efforts outside California.

Crashes on and near college campuses: : a comparative analysis of pedestrian and bicyclist safety

Problem, research strategy, and findings: College campuses are multimodal settings with very high levels of walking and biking in conjunction with high levels of vehicular traffic, which increases risks for bicyclists and pedestrians. In this study, we examine crash data (both police reported and self-reported) and urban form data from three U.S. campuses to understand the spatial and temporal distribution of crashes on the campuses and their immediate periphery. To account for underreporting of pedestrian and bicycle crashes, we developed and circulated an online survey, which helped identify collision hotspots across the three campuses. We then studied these locations to determine their characteristics, generate a typology of campus danger zones, and recommend design and policy changes that could improve pedestrian and cycling safety. We find a significant underreporting of crashes, and unequal spatial and temporal distributions of campus crashes. We identify three particular types of danger zones for pedestrians and cyclists: campus activity hubs, campus access hubs, and through traffic hubs. Injuries tended to be more serious for those crashes taking place on campus peripheries. Takeaway for practice: The intermingling of motorized and non-motorized modes creates significant opportunities for crashes. Planners should be aware of the existing underreporting and give special attention to the three types of danger zones. In addition to the recommendations of the literature for the creation of campus master plans for walking and biking, campuses should conduct safety audits and surveys to identify hotspots and consider specific design improvements for each of the three danger zones to lessen modal conflict.

Methodology to Gather Multimodal Trip Generation Data in Smart-Growth Areas

This study presents a method to quantify multimodal trip generation for developments in smart-growth areas. The technique combines door counts and intercept surveys to classify trips by mode, and it has several advantages over existing methods that use automated technologies to count automobiles entering and exiting access points to developments. These advantages are particularly important in urban areas with mixed-use developments, mixed-use buildings, and a variety of parking arrangements. First, door counts quantify the total number of trips generated by all modes. Second, door counts quantify all people traveling to and from particular land uses, even if a targeted use is part of a larger, mixed-use building. Third, intercept surveys differentiate between people who are walking for an entire trip and people who are walking as a secondary mode to or from parking or transit. The method was applied at 30 smart-growth study locations in California. Multimodal person trips and vehicle trips were documented at 24 of the study locations during the morning peak hour and at 27 study locations during the afternoon peak hour. Weighted averages from these locations show that suburban-based ITE peak hour vehicle trip estimates were 2.3 times higher than actual vehicle trips in the morning and 2.4 times higher than those in the afternoon. Total person trip generation at the smart-growth study locations was similar to the total person trips estimated from ITE data; however, larger shares of person trips at the smart-growth locations were made by walking, bicycling, or public transit.

Investigating the underreporting of pedestrian and bicycle crashes in and around university campuses − a crowdsourcing approach

In this paper, the non-motorized traffic safety concerns in and around three university campuses are evaluated by comparing police-reported crash data with traffic safety information sourced from the campus communities themselves. The crowdsourced traffic safety data comprise of both self-reported crashes as well as perceived hazardous locations. The results of the crash data analysis reveal that police-reported crashes underrepresent non-motorized safety concerns in and around the campus regions. The spatial distribution of police-reported crashes shows that police-reported crashes are predominantly unavailable inside the main campus areas, and the off-campus crashes over-represent automobile involvement. In comparison, the self-reported crash results report a wide variety of off-campus collisions not involving automobiles, while also highlighting the issue of high crash concentrations along campus boundaries. An assessment of the perceived hazardous locations (PHLs) reveals that high concentrations of such observations at/near a given location have statistically significant association with both survey-reported crashes as well as future police-reported crashes. Moreover, the results indicate the presence of a saturation point in the relationship between crashes and PHLs wherein beyond a certain limit, an increasing number of traffic safety concerns may not necessarily correlate with a proportional increase in the number of crashes. These findings suggests that augmenting our existing knowledge of traffic safety through crowdsourcing techniques can potentially help in better estimating both existing as well as emerging traffic safety concerns.

Incorporating safety into targeted pavement friction data collection and maintenance procedures

The objective of this research was to develop a methodology for targeted pavement friction data collection based on the analysis of weather-related crashes. Furthermore, the aim was to identify threshold values of pavement friction characteristics indicating a significant impact on safety prompting the need for maintenance and improvements. Spatial analysis using Local Moran’s I statistic identified hotspots where pavement friction data were collected. A master database was assembled including Wisconsin State Trunk Network (STN) road attributes, hotspots of weather-related crashes, and pavement friction data collected based on hotspot analysis. The analysis results provide evidence in support of hotspot analysis as a viable procedure for targeted pavement friction data collection to enable efficiency and cost reductions. Classification tree analysis using GUIDE (Generalized, Unbiased, Interaction Detection and Estimation) algorithm was used to further explore the relationship between pavement friction characteristics and safety. Statistically significant hotspots were observed below a pavement friction number of approximately 57 and very high hotspots below a pavement friction number of approximately 42. The results indicate that pavement friction thresholds identified in the literature between 20 and 32 may be too low and that safety may be impacted at friction numbers as high as in the forties. The results also show differences in friction and safety for various types of pavement surfaces. The use of weather-related crashes provides a data-driven and cost-effective method of prioritizing locations for pavement friction data collection and maintenance. Results from this research can be readily used in initial steps of systemic road safety management procedures by practitioners.

ANALYSIS OF GROSS POLLUTER CUTPOINTS BASED ON IN-USE VEHICLE INSPECTION AND MAINTENANCE TEST DATA IN CALIFORNIA

Emission cutpoints are used to classify vehicles as “gross polluters” in inspection and maintenance (I/M) testing in the United States. In this study, we use recent data from California I/M tests to examine how sensitive populations of “gross polluters” change as cutpoints are modified. Using statistical analyses of emissions test data, we identify a variety of scenarios that have the potential for efficiently removing a significant portion of the exhibited emissions while minimizing the number of vehicles that are classified as “gross polluters” and required to undergo costly repairs. For each scenario, we compute the resulting vehicle populations and impact on emissions. The conclusions of the research provide support for previous findings that suggests that: 1) a “gross polluting” vehicle for one pollutant, such as carbon monoxide (CO), may not necessarily be a “gross polluter” for another pollutant, such as oxides of nitrogen (NOx) and 2) each of the existing required I/M tests yield similar results and it may not be efficient to use both tests in the identification of gross polluters.