Archived project

Predicting Accident Rates for Pedestrians and Cyclists

Goal: Development of SPFs/CPMs for pedestrians and cyclists and also looking at reporting rates of crashes using hospital data

Date: 1 January 2004 - 1 January 2006

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12

Project log

Shane Turner
added a research item
SUBCOMMITTEE MEETING - SUMMARY OF FIVE KEY STUDIES Pedestrian and Bicycle Flow-only SPFs Mid-block Bicycle Safety – geometric features Bicycle Safety at Roundabouts Safety of Cycle Facilities at Traffic signals Pedestrian Safety at Traffic Signals
Shane Turner
added 2 research items
During the late 1990s generalised linear models were developed for the major motor-vehicle-only accidents types at urban intersections in New Zealand. Generally there were four or five major motor-vehicle-only accident types. At traffic signals and roundabouts, particularly in Christchurch, the next most common accident types often involved pedestrians or cyclists. This paper discusses a study undertaken for Transfund to produce generalised linear models for the major cycle and pedestrian accident types. A sample of traffic signal, roundabout and mid-block locations was selected in Christchurch, Hamilton and Palmerston North; three flat cities within New Zealand with a relatively high number of cyclists. The mid-block selection criteria focused on arterial routes with 'strip' shopping. Pedestrian and cycle count data was collected at each site, to accompany motor-vehicle count data. Data was collected on a number of non-flow variables, including visibility, number of approach lanes, crossing distance, and compliance with signalised crossing 'green man'. Using the collected and existing data, accident prediction models were developed using generalised linear models for accidents between cyclists and motor vehicles and pedestrians and motor vehicles. These models are presented in this paper.
Pedestrians and cyclists involved in crashes with motor-vehicle were surveyed over two 4 week periods at Christchurch hospital (emergency department). Telephone surveys were also undertaken of people who had reported a cycle or pedestrian crash to ACC in 2002. The survey was designed to capture more detailed information on crashes than was available from Police records, and focused on more severe crashes. The questionnaire covered the following areas; demographics, travel mode, cycle type, date, time and location of crash, light conditions, weather, crash type, footpath and road conditions, main crash causes, estimate of vehicle and cycle speeds, trip purpose, details on injuries sustained and emergency services that attended (to check with other databases). This technical note presents the key findings from this survey. The technical note also provides information on reporting rates for serious pedestrian and cycle crashes and the compliance rates (those crossing on green man) of pedestrians at traffic signals by time of day.
Shane Turner
added a research item
Pedestrians are over-represented in the New Zealand accident statistics given the proportion of walking trips, particularly on a distance-travelled basis. In this study Beca developed prediction models for pedestrian versus motor vehicle accidents at urban traffic signals and mid-block road sections in commercial (shopping) areas. The accident rate for pedestrian accidents was significantly higher than for motor vehicle only accidents. However, the research indicates that the pedestrian accident risk (accidents per pedestrian) drops considerably when the pedestrian volume increases, and this drop is much more pronounced than for motor vehicle only accidents. During the study information on pedestrian only and off-road pedestrian accidents was also collected from hospital interviews and other data sources. This paper will present the accident prediction models produced for each accident type, pedestrian hourly profiles and a number of other tables and graphs that illustrate trends in accident occurrences.
Shane Turner
added a project goal
Development of SPFs/CPMs for pedestrians and cyclists and also looking at reporting rates of crashes using hospital data