INTRODUCTIONPlanning QuestionsResearch Methodology QuestionsSURVEY DESIGNDeterminantsComfort and PredictabilitySafety and TimeMid-block. Chu and Baltes (22) identify potential determinants for pedestrian crossing behavior at mid-block locations, based on supply of gaps, crossing time, and safety margin, which form the three components of the gap-acceptance behavior of pedestrIntersections. Crider et al. identify potential dRoadside. Landis et al. identify potential determinants for pedestrians walking along roadsides (23). Through a step-wise regression process, the authors identify factors describing the roadside environment, including the various components of lateral SitesScenariosStart and End PointsCrosswalk MarkingCrossing OptionsGroup Crossing ScenariosInstrumentsAFDCBEDATA COLLECTIONStatic DataField SurveysData SetBlocksParticipantsDescriptive StatisticsESTIMATIONHypothesesStatistical ModelIndependent VariablesModel EstimationDISCUSSIONLimitationsImplicationsResearch MethodsA number of implications can be drawn that have both current and lasting value to researchers:Planning ToolsThe existing tools for determining pedestrian level of service are based on simple regression models that predict pedestrian perceptions of quality of service with the street environment. The estimated model from this research could provide a new approacEngineering SolutionsThe estimated model may be used to simulate how certain engineering solutions may influence how pedestrians cross streets.ACKNOWLEDGMENTSREFERENCESThe Role of the Street Environment in How People Cross Roads in Urban Settings Michael R. Baltes and Xuehao Chu Center for Urban Transportation Research University of South Florida 4202 East Fowler Avenue, CUT 100 Tampa, FL 33620 [email protected], [email protected] Martin Guttenplan Florida Department of Transportation Systems Planning Office 605 Suwannee Street, MS-19 Tallahassee, FL 32399 [email protected] ABSTRACT This paper models the role of the street environment in how people cross roads in urban settings. Respondents were placed in real traffic conditions at the curbside of street blocks in the Tampa Bay area for a three-minute observation of the street environment. Without crossing the blocks, each respondent stated his crossing preference at each of six blocks. The origin and destination of each crossing were hypothetically set and varied across the blocks. So were the options available: two options for crossing at an intersection and up to four options for crossing at mid-block locations. Within the framework of discrete-choice models, the stated preferences are explained with the street environment, including traffic conditions, roadway characteristics, and signal-control characteristics. All three components of the street environment are considered: mid-block locations, intersections, and the roadside environment. This paper describes survey design and data collection efforts; estimates a nested logit model of pedestrian street-crossing behavior; and discusses its implications to researchers and practitioners. Key words: nested logit model—pedestrian safety—street crossing—street environment Proceedings of the 2003 Mid-Continent Transportation Research Symposium, Ames, Iowa, August 2003. © 2003 by Iowa State University. The contents of this paper reflect the views of the author(s), who are responsible for the facts and accuracy of the information presented herein.INTRODUCTION Street crossing is a critical element of the urban transportation environment for pedestrians. A large body of work already exists on street crossing by pedestrians, including the following by subject area: • crossing delays (1) • crossing opportunities (2) • pedestrians’ behavioral parameters (3–4) • pedestrian compliance (5) • pedestrian perceptions toward specific treatments (6) • determination of level of service (7–10) • engineering parameters such as pedestrian clearance intervals (11) • evaluation of treatments (12–13) • drivers’ perspective (14–15) • safety (16) • empirical modeling (17–19) However, little research exists that can help answer questions related to pedestrian planning, engineering solutions to pedestrian crossing safety, and research methods for modeling street-crossing behavior. Below are a few examples of these questions: Planning Questions • How can existing planning tools for determining pedestrian level of service for street crossing at mid-block locations and intersections be integrated to determine pedestrian level of service at the block level? Engineering Questions • How and when might a pedestrian go to a crosswalk at mid-block locations? • How and when might a pedestrian go to an intersection? • Where should transit bus stops be located so that transit users are more likely to choose safe crossing options to access them? Research Methodology Questions • What statistical models are most appropriate for modeling the street-crossing behavior of pedestrians so that these planning and engineering questions can be answered? • What and how should data be collected in order to estimate such statistical models? This paper models the role of the street environment in how pedestrians cross roads in urban settings. Specifically, 86 participants placed in real traffic conditions at the curbside of 48 street blocks in the Tampa Bay area observed the street environment for three minutes. Without crossing the street blocks, the participants stated their crossing preferences at each of six blocks. The origin and destination for each crossing were hypothetically set and varied across the blocks. So were the options available: two options for crossing at intersections and up to four options for crossing at mid-block locations. Within the framework of discrete choice models, the stated preferences are explained by traffic conditions, roadway characteristics, and signal-control characteristics. The paper focuses on the street environment so that variables are readily measured for model applications. As an alternative, one could model the role of the direct attributes, such as safety and time, that Baltes, Chu, and Guttenplan 2pedestrians may tradeoff in choosing a crossing option. By focusing on the street environment, the paper assumes that the indirect attributes that characterize the street environment determine the direct attributes and that the street crossing behavior can be