Thomas Kjær Rasmussen

Improved methods to deduct trip legs and mode from travel surveys using wearable GPS devices: A case study from the Greater Copenhagen area

Abstract GPS data collection has become an important means of investigating travel behaviour. This is because such data ideally provide far more detailed information on route choice and travel patterns over a longer time period than possible from traditional travel survey methods. Wearing a GPS unit is furthermore less requiring for the respondents than filling out (large) questionnaires. It places however high requirements to the post-processing of the data. This study developed and tested a combined fuzzy logic and GIS-based algorithm to process raw GPS data. The algorithm is applied to GPS data collected in the highly complex large-scale multi-modal transport network of the Greater Copenhagen area. It detects trips, trip legs and distinguishes between five modes of transport. The algorithm was validated by comparing with a control questionnaire collected among the same persons and a sensitivity analysis was performed. This showed that the algorithm (i) identified corresponding trip legs for 82% of the reported trip legs, (ii) avoided classifying non-trips such as scatter around activities as trip legs, (iii) identified the correct mode of transport for more than 90% of trip legs, and (iv) were robust towards the specification of the model parameters and thresholds. The method thus makes it possible to use GPS for travel surveys in large-scale multi-modal networks.

Estimating Value of Congestion and of Reliability from Observation of Route Choice Behavior of Car Drivers

In recent years, a consensus has been reached about the relevance of calculating the value of congestion and the value of reliability for better understanding and therefore better prediction of travel behavior. The current study proposed a revealed preference approach that used a large amount of GPS data from probe vehicles to provide insight into actual behavior in choosing a route. Mixed path size correction logit models were estimated from samples of 5,759 observations in the peak period and 7,964 observations in the off-peak period, while a mean-variance model was specified to consider both congestion and reliability terms. Results illustrated that the value of time and the value of congestion were significantly higher in the peak period because of possible higher penalties for drivers being late and consequently possible higher time pressure. Moreover, results showed that the marginal rate of substitution between travel time reliability and total travel time did not vary across periods and traffic conditions, with the obvious caveat that the absolute values were significantly higher for the peak period. Last, results showed the immense potential of exploiting the growing availability of large amounts of data from cheap and enhanced technology to obtain estimates of the monetary value of different travel time components from the observation of actual behavior, with arguably potential significant impact on the realism of large-scale models.

Infrastructure and spatial effects on the frequency of cyclist-motorist collisions in the Copenhagen Region

ABSTRACT Promoting cycling aims at reducing congestion and pollution as well as encouraging healthy and sustainable lifestyles but generally clashes with the perception of crash risk while riding a bicycle that is still the most significant disincentive to cycling. This study sheds light on the factors affecting the probability of cyclist–motorist collisions while accounting for heterogeneity and spatial correlation. The current study analyzed the factors contributing to increase crash risk while riding a bicycle by focusing on 5,349 cyclist–motorist collisions within 269 traffic zones in the Copenhagen Region. The model controlled for traffic exposure for bicycles and motorized transport modes, evaluated the effects of infrastructure and socioeconomic characteristics of the zones, and accounted for heterogeneity and spatial correlation across the zones. A Poisson-lognormal model with second-order conditional autoregressive (CAR) priors confirmed the existence of the safety in numbers phenomenon, contradicted previous literature about bicycle facilities not being helpful in reducing crash risk, highlighted the need for Copenhagen-style bicycle paths especially in suburban areas, and emphasized how heterogeneity and spatial correlation play a significant role in explaining the probability of cyclist-motorist crash occurrence.

Risk factors associated with crash severity on low-volume rural roads in Denmark

Safety on low-volume rural roads is drawing attention due to the high fatality and severe injury rates in comparison with high-volume roads and the increasing awareness of sustainable rural development among policy makers. This study analyzes the risk factors associated with crash severity on low-volume rural roads, including crash characteristics, driver attributes and behavior, vehicle type, road features, environmental conditions, distance from the nearest hospital, and zone rurality degree. The data consist of a set of crashes occurred on low-volume rural roads in Denmark between 2007 and 2011. The crashes were identified by map-matching the crash location to the geographic information system representing the national transport network and extracting the relevant crashes based on annual average traffic volumes. Injury severity was modeled by estimating a generalized ordered logit model due to its advantage in accommodating the ordered-response nature of severity while relaxing the proportional odds assumption. Model estimates and pseudoelasticities show that aggravated crash injury severity is significantly associated with (1) alcohol and failure to wear seatbelts, (2) involvement of vulnerable road users (i.e., pedestrians, cyclists and motorcyclists), (3) involvement of heavy vehicles, (4) speed limits of 80–90 km/h, (5) longer distance to the nearest hospital, and (6) peripheral rural regions.

Considering built environment and spatial correlation in modeling pedestrian injury severity

ABSTRACT Objective: This study looks at mitigating and aggravating factors that are associated with the injury severity of pedestrians when they have crashes with another road user and overcomes existing limitations in the literature by focusing attention on the built environment and considering spatial correlation across crashes. Method: Reports for 6,539 pedestrian crashes occurred in Denmark between 2006 and 2015 were merged with geographic information system resources containing detailed information about the built environment and exposure at the crash locations. A linearized spatial logit model estimated the probability of pedestrians sustaining a severe or fatal injury conditional on the occurrence of a crash with another road user. Results: This study confirms previous findings about older pedestrians and intoxicated pedestrians being the most vulnerable road users and crashes with heavy vehicles and in roads with higher speed limits being related to the most severe outcomes. This study provides novel perspectives by showing positive spatial correlations of crashes with the same severity outcomes and emphasizing the role of the built environment in the proximity of the crash. Conclusions: This study emphasizes the need for thinking about traffic calming measures, illumination solutions, road maintenance programs, and speed limit reductions. Moreover, this study emphasizes the role of the built environment, because shopping areas, residential areas, and walking traffic density are positively related to a reduction in pedestrian injury severity. Often, these areas have in common a larger pedestrian mass that is more likely to make other road users more aware and attentive, whereas the same does not seem to apply to areas with lower pedestrian density.