This paper describes a practical method of adjusting existing Institute of Transportation Engineers (ITE) estimates to produce more accurate estimates of motor-vehicle trip-generation at developments in smartgrowth areas. Two linear regression equations, one for an A.M. peak-hour adjustment and one for a P.M. peak-hour adjustment, were developed using vehicle trip counts and easily measured site and surrounding area context variables from a sample of 50 smart-growth sites in California. Many of the contextual variables that were associated with lower vehicle trip generation at the smart-growth study sites were correlated. Therefore, variables representing characteristics such as residential population density, employment density, transit service, metered on-street parking, and building setback distance from the sidewalk were combined into a single "smart-growth factor" that was used in the linear regression equations. The A.M. peak-hour and P.M. peak-hour adjustment equations are only appropriate for planning-level analysis at sites in smart-growth areas. In addition, the method is only appropriate for single land uses in several common categories, such as office, mid-to highdensity residential, restaurant, and coffee/donut shop. The method uses data from California, but the methodological approach could provide a framework for adjusting ITE trip-generation estimates in smart-growth areas throughout the United States.

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.

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.

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.

Problem, research strategy, and fi ndings: College campuses are multimodal settings with very high levels of walking and biking in conjunction with high levels of vehicular traffi c, 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 fi nd a signifi cant 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 traffi c 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 signifi cant 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 specifi c design