Spatial patterns and temporal dynamics of urban bicycle crashes—A case study from Salzburg (Austria)

Most bicycle crash analyses are designed as explanatory studies. They aim to identify contributing risk factors and calculate risk rates based on – most of the time – highly aggregated statistical data. In contrast to such explanatory study designs, the presented study follows an exploratory approach, focusing on the absolute number of crashes. The aim is to reveal and describe patterns and dynamics of urban bicycle crashes on various spatial scale levels and temporal resolutions through a multi-stage workflow. Spatial units are delineated in the network space and serve as initial units of aggregation. In order to facilitate comparisons among regions and quantify temporal dynamics, a reference value of crash frequency is simulated for each unit of the respective spatial scale level and temporal resolution.For the presented case study, over 3000 geo-coded bicycle crashes in the city of Salzburg (Austria) were analyzed. The data set covers 10 years and comprises all bicycle crashes reported by the police. Distinct spatial and temporal patterns with clusters, seasonal variations, and regional particularities could be revealed. These insights are indicators for urban dynamics in the transport system and allow for further, targeted in-depth analyses and subsequent counter measures. Moreover, the results prove the applicability of the proposed multi-stage workflow and demonstrate the added value of analyses of small aggregates on various scale levels, down to single crashes, and temporal resolutions.     

Analysis of an integrated maximum covering and patrol routing problem

In this paper, we address the problem of determining the patrol routes of state troopers for maximum coverage of highway spots with high frequencies of crashes (hot spots). We develop a specific mixed integer linear programming model for this problem under time feasibility and budget limitation. We solve this model using local and tabu-search based heuristics. Via extensive computational experiments using randomly generated data, we test the validity of our solution approaches. Furthermore, using real data from the state of Alabama, we provide recommendations for (i) critical levels of coverage; (ii) factors influencing the service measures; and (iii) dynamic changes in routes.     

Are individual preferences always a legitimate basis for evaluating the costs and benefits of public policy?: The case of road traffic law enforcement

This paper discusses if it is appropriate to include the benefits that offenders get by violating the law in cost-benefit analyses of police enforcement, in the form of a loss of benefits from violations. The discussion is cast in the context of traffic law violations, a very common type of crime, which is usually not very strongly condemned from a moral point of view. Three options for cost-benefit analysis of traffic police enforcement are compared. The three options differ with respect to the treatment of (a) violator benefits from violations, and (b) outlays for traffic tickets given to violators. The implications of the choice of option are shown by means of four case illustrations, all referring to different types of traffic police enforcement in Norway. It is shown that every type of enforcement becomes less cost-effective when violator benefits are included in cost-benefit analyses. The legitimacy of including violator benefits as a benefit to society is discussed. It is concluded that, as far as traffic violations are concerned, benefits obtained by committing violations of the law cannot be treated as a legitimate societal benefit.     

Modeling bicycle crash costs using big data: A grid-cell-based Tobit model with random parameters

Bicyclists are among the most vulnerable road users in the urban transportation system. It is critical to investigate the contributing factors to bicycle-related crashes and to identify the hotspots for efficient allocation of treatment resources. A grid-cell-based modeling framework was used to incorporate heterogeneous data sources and to explore the overall safety patterns of bicyclists in Manhattan, New York City. A random parameters (RP) Tobit model was developed in the Bayesian framework to correlate transportation, land use, and sociodemographic data with bicycle crash costs. It is worth mentioning that a new algorithm was proposed to estimate bicyclist-involved crash exposure using large-scale bicycle ridership data from 2014 to 2016 obtained from Citi Bike, which is the largest bicycle sharing program in the United States. The proposed RP Tobit model could deal with left-censored crash cost data and was found to outperform the Tobit model by accounting for the unobserved heterogeneity across neighborhoods. Results indicated that bicycle ridership, bicycle rack density, subway ridership, taxi trips, bus stop density, population, and ratio of population under 14 were positively associated with bicycle crash cost, whereas residential ratio and median age had a negative impact on bicycle crash cost. The RP Tobit model was used to estimate the cell-specific potential for safety improvement (PSI) for hotspot ranking. The advantages of using the RP Tobit crash cost model to capture PSI are that injury severity is considered by being converted into unit costs, and varying effects of certain explanatory variables are accounted for by incorporating random parameters. The cell-based hotspot identification method can provide a complete risk map for bicyclists with high resolution. Most locations with high PSIs either had unprotected bicycle lanes or were close to the access points to protected bicycle routes.     

A GIS-based Bayesian approach for analyzing spatial–temporal patterns of intra-city motor vehicle crashes

This paper develops a GIS-based Bayesian approach for intra-city motor vehicle crash analysis. Five-year crash data for Harris County (primarily the City of Houston), Texas are analyzed using a geographic information system (GIS), and spatial–temporal patterns of relative crash risks are identified based on a Bayesian approach. This approach is used to identify and rank roadway segments with potentially high risks for crashes so that preventive actions can be taken to reduce the risks in these segments. Results demonstrate the approach is useful in estimating the relative crash risks, eliminating the instability of estimates while maintaining overall safety trends. The 3-D posterior risk maps show risky roadway segments where safety improvements need to be implemented. Results of GIS-based Bayesian mapping are also useful for travelers to choose relatively safer routes.     

Macro-level analysis of bicycle safety: Focusing on the characteristics of both crash location and residence

Over the last decade, bicycle ridership has been encouraged as a sustainable mode of transportation as it is economic and has less impact on the environment. Still, higher crash risk for bicyclists remains a deterrent for people to choose bicycling as their major mode of travel. As a first step in investigating bicycle safety, it is essential to identify not only the characteristics of the areas with the excessive number of bicycle crashes; but also those of the areas where crash-prone bicyclists reside. Therefore, this study aims to identify contributing factors for two subjects: (1) the number of bicycle crashes in the crash location's ZIP code and (2) the number of crash-involved bicyclists in their residence's ZIP. In order to achieve these objectives, a multivariate Bayesian Poisson lognormal CAR (conditional autoregressive) model was developed to identify the contributing factors for each subject. Regarding the model performance, the multivariate model outperformed its univariate counterpart in terms of DIC (deviance information criterion). Subsequently, hot zones for the two target zones were identified based on the modeling results. It is expected that practitioners are able to understand the contributing factors for bicycle crashes and identify hotspots from the results suggested in this study. In addition, they could implement safety countermeasures for the identified problematic locations to effectively reduce bicycle crashes.     

Managing crime and the fear of crime at railway stations––a case study in South Wales (UK)

Rail users consistently perceive their risks from crime to be significantly higher than official statistics suggest, discouraging many from using rail transport. The aims of the paper include a discussion of Crime Prevention Through Environmental Design (CPTED) and current policy initiatives for reducing crime and the fear of crime on the railways. This exploratory study focuses on adult passengers' perceptions of crime and nuisance as they relate to the management, design and maintenance of railway stations and their immediate access routes. The study innovatively utilises interactive virtual reality (VR) scenes of `representative' stations as the environmental stimulus and concludes that such an approach provides an analytical and pragmatic framework for managers of railway stations that are unlikely to receive Secure Station accreditation.     

Integrating police reports with geographic information system resources for uncovering patterns of pedestrian crashes in Denmark

Promoting walking goes a long way in contributing to the sustainability and health of future cities and regions, and improving pedestrian safety is essential for building more sustainable and healthier communities. As the problem is multifaceted in nature, this study looks at patterns of pedestrian crashes from a perspective that goes beyond the traditional investigation of pedestrian characteristics and behaviour by analysing the contribution of built environment, land use, and traffic conditions. Moreover, this study goes beyond the traditional analysis of traditional police reports by integrating them with rich geographic information system resources. This study analysed a sample of 7469 crashes between a pedestrian and another road user that occurred in Denmark between 2006 and 2015. The crash locations were geocoded and matched to a detailed traffic network, a transport planning model, and several resources detailing building and land use composition. Latent class analysis uncovered patterns of pedestrian crashes for both the fully identified records and the substantial amount of hit-and-run records. Findings from this study reveal a major red thread in the lack of hazard awareness for both pedestrians and road users and suggest solutions from both the behavioural and the infrastructure perspectives. Major needs are (i) educating pedestrians about the risks related to drinking and then walking along major roads in the darkness, (ii) making crossings for pedestrians and approaches for road users easier to understand and to access in order to reduce unnecessary conflicts, and (iii) designing traffic calming solutions around major shopping and leisure locations in dense city centres.     

Exploring the spatial impacts of human activities on urban traffic crashes using multi-source big data

Traffic crashes are geographical events, and their spatial patterns are strongly linked to the regional characteristics of road network, sociodemography, and human activities. Different human activities may have different impacts on traffic exposures, traffic conflicts and speeds in different transportation geographic areas, and accordingly generate different traffic safety outcomes. Most previous researches have concentrated on exploring the impacts of various road network attributes and sociodemographic characteristics on crash occurrence. However, the spatial impacts of human activities on traffic crashes are unclear. To fill this gap, this study attempts to investigate how human activities contribute to the spatial pattern of the traffic crashes in urban areas by leveraging multi-source big data. Three kinds of big data sources are used to collect human activities from the New York City. Then, all the collected data are aggregated into regional level (ZIP Code Tabulation Areas). Geographically Weighted Poisson Regression (GWPR) method is applied to identify the relationship between various influencing factors and regional crash frequency. The results reveal that human activity variables from multi-source big data significantly affect the spatial pattern of traffic crashes, which may bring new insights for roadway safety analyses. Comparative analyses are further performed for comparing the GWPR models which consider human activity variables from different big data sources. The results of comparative analyses suggest that multiple big data sources could complement with each other in the coverage of spatial areas and user groups, thereby improving the performance of zone-level crash models and fully unveiling the spatial impacts of human activities on traffic crashes in urban areas. The results of this study could help transportation authorities better identify high-risky regions and develop proactive countermeasures to effectively reduce crashes in these regions.     

Effects of winter precipitation on automobile collisions,injuries, and fatalities in the United States

To better understand the links between winter precipitation (snow, sleet, and freezing rain) and travel risk, data on weather conditions and vehicle crashes, injuries and fatalities are gathered for 13 U.S. cities. A matched pair analysis is used to construct event-control pairs to determine the relative risk of crash, injury, and fatality. Winter precipitation is associated with a 19% increase in traffic crashes and a 13% increase in injuries compared to dry conditions. The type of winter precipitation (snowfall vs. freezing rain, ice pellets, or sleet) had no significant impact on the relative risk of crash. The relative risk of crash was significantly higher during the evening (1800–2359 local time) than during other times of the day. More intense precipitation led to increased relative risk of crash and injury compared to less intense precipitation. Relative risk of crash, injury, or fatality was not significantly higher during the first three winter precipitation events of the year as compared to subsequent events. The relative risk of both winter precipitation crash and injury showed no significant trend during the 1998–2008 period. Sensitivity of U.S. cities to winter precipitation varies from city to city in a manner that is not easily explained. Future research will be required to determine which safety interventions are most effective in each city and revise or expand safety programs appropriately.     

Assessing the road safety impacts of a teleworking policy by means of geographically weighted regression method

Travel demand management (TDM) consists of a variety of policy measures that affect the effectiveness of transportation systems by changing travel behavior. The primary objective of such TDM strategies is not to improve traffic safety, although their impact on traffic safety should not be neglected. The main purpose of this study is to simulate the traffic safety impact of conducting a teleworking scenario (i.e. 5% of the working population engages in teleworking) in the study area, Flanders, Belgium. Since TDM strategies are usually conducted at a geographically aggregated level, crash prediction models should also be developed at an aggregate level. Given that crash occurrences are often spatially heterogeneous and are affected by many spatial variables, the existence of spatial correlation in the data is also examined. The results indicate the necessity of accounting for the spatial correlation when developing crash prediction models. Therefore, zonal crash prediction models (ZCPMs) within the geographically weighted generalized linear modeling framework are developed to incorporate the spatial variations in association between the number of crashes (including fatal, severe and slight injury crashes recorded between 2004 and 2007) and other explanatory variables. Different exposure, network and socio-demographic variables of 2200 traffic analysis zones (TAZs) are considered as predictors of crashes. An activity-based transportation model framework is adopted to produce detailed exposure metrics. This enables to conduct a more detailed and reliable assessment while TDM strategies are inherently modeled in the activity-based models. In this study, several ZCPMs with different severity levels and crash types are developed to predict crash counts for both the null and the teleworking scenario. The results show a considerable traffic safety benefit of conducting the teleworking scenario due to its impact on the reduction of total vehicle kilometers traveled (VKT) by 3.15%. Implementing the teleworking scenario is predicted to reduce the annual VKT by 1.43 billion and the total number of crashes to decline by 2.6%.     

试论警犬技术在铁路治安保卫中的作用及其应用

警犬具有听觉、嗅觉发达，防御机能强，扑咬凶猛等得天独厚的特点，现广泛地应用于铁路治安保卫工作中，可执行巡逻、警戒、看守、处理突发事件等任务，能够降低发案率、预防和打击犯罪。本文从警犬技术的特点和作用出发，论述了警犬在铁路治安保卫工作中的使用范围、方法和程序。     

Testing theft transmission in and around São Paulo metro stations,Brazil

Metro stations are criminogenic places that radiate and/or absorb crime. In this paper, we assess potential theft transmission in these places and their surrounding areas using police recorded data from the São Paulo metro system in Brazil. The study classifies whether metro stations are crime absorbers, crime radiators, or both, by time of day and day of the week. Time series data were used to test the empirical model (panel vector autoregressive) and Granger tests for temporal precedence. Findings show that ‘theft waves’ recorded in the metro stations often precede in time those that occur in the stations' surrounding areas. However, on weekdays, during off-peak hours, the opposite might happen: stations absorb some of the crime that spills over from criminogenic surrounding areas into the stations. Since there is evidence of transmission of crime from one environment to another, in both directions, better cooperation between police responsible for security in the surrounding areas and the metro security staff might improve crime prevention initiatives in these transit settings.     

论铁路公安技术工作发展的若干问题

目前铁路公安技术工作在体制、管理、装备、人才、信息、保障等方面还存在很多的不适应，需要各级领导提高认识、加大投入，大力推进铁路公安技术专业化、规范化、信息化、现代化建设。只有强化铁路公安技术工作，才能更好地实现铁路公安机关维护铁路和社会稳定的需要，才能进一步提高铁路公安机关的执法水平，提高铁路公安机关的战斗力，有力地促进铁路公安机关现代警务机制的建立。     

Factors contributing to injury severity in work zone related crashes in New Zealand

Roadworks take place frequently on existing roads in New Zealand. The adverse effects of poor road conditions and reduced road width due to the presence of a work zone on the safety of road users and workers at the work zone have been a matter of concern. Several studies have been conducted to examine the risk factors contributing to the occurrence of road crashes in work zones in different countries. Slow-moving and stopped vehicles near work zones have been found to be the primary cause of crashes and casualties in the work zones. Excessive speed of passing traffic has also been recognized as a crucial factor contributing to work zone related crashes in New Zealand. This study examined the effect of possible risk factors contributing to severe injury and fatality in work zone related crashes in New Zealand. A multinomial logistic regression model was established to determine the association between crash severity and factors such as road environment, vehicle attributes, driver behavior, and crash circumstances, based on the information available on 453 road crashes during the period from 2008 to 2013. The results indicated that the time period, vehicle involvement, and presence of vulnerable road users were the factors that determined the crash severity in work zones. This implies that improvements are required in traffic control and management measures in work zones to enhance road safety in the long run.     

A zonal level safety investigation of pedestrian crashes in Riyadh,Saudi Arabia

In the recent decade, walking has been encouraged as an active mode of transportation, which could reduce congestion and air pollution and also improve community health. However, pedestrians are more vulnerable to traffic crashes compared with other road users, especially in developing countries such as Saudi Arabia. This paper examines the association among traffic volume, land-use, socio-demographic and roadway characteristics factors, and the frequency of pedestrian crashes based on macro-level safety analysis using data from Riyadh, the Capital of Saudi Arabia. Two Bayesian spatial Poisson-lognormal models for total and severe pedestrian crashes are developed in this study. The results show that the factors that affect total pedestrian crash occurrence are different from those affecting severe pedestrian crash. Several implications for pedestrian safety policies in Riyadh are suggested based on the results.     

Factors associated with disability paratransit's travel time reliability

This paper identifies some of the characteristics of trips and pick-up and drop-off locations that are associated with paratransit's travel time reliability. Following convention, reliability has been defined as the inverse of variability. Four measures of travel time variability have been used to examine reliability: Standard Deviation, Percent Variation, Misery Index, and Buffer Index. Regression models have been used to estimate these four variables with trip data from Access Link, the paratransit service provided by NJ TRANSIT pursuant to the Americans with Disabilities Act (ADA). A number of characteristics of the pick-up and drop-off locations as well as selected characteristics of the trips were used as independent variables of the models. The statistical significance of the independent variables varied depending on which measure of reliability was estimated, but a few variables were consistently associated with reliability in all four models. These variables were trip distance, booking type, winter season, density of motor vehicle crashes in pick-up and drop-off locations, and whether pick-ups occurred in suburban bus corridors or urban core areas. Because of the significance of the variables on motor vehicle crash density in pick-up and drop-off locations, an additional regression model was used to examine the effect of crash incidents on trip duration by considering drop-offs that occurred in locations immediately after a crash. The model showed that trips take 4 to 5% longer when crashes occur in locations prior to a drop off. Planning implications of the findings are discussed.     

The safety impact of land use changes resulting from bypass road constructions

This paper analyzes the effect of bypass construction on road safety, specifically crash rates on bypass segments and in bypassed communities. It further investigates the effect of bypass construction on those communities’ economic development through changes in the number and spatial distribution of businesses and residential development, and examines whether these changes affect the road-safety level. Finally, it evaluates the effect on road safety of various traffic arrangements implemented in such communities. Drawing on an extensive data collection, the study focuses on Arab cities in the Galilee region of Israel, using cross-sectional and longitudinal analysis.The results show that bypass construction does not necessarily reduce overall crash frequencies or crash rates. It merely shifts road crashes from the highways (the bypass roads) to inner roads (bypassed) and from there to local traffic, with no significant reduction. The findings indicate a large variance in the effects of bypass constructions on safety.     

Investigating the associations between road network structure and non-motorist accidents

Road networks channel traffic flow and can impact the volume and proximity of walking and bicycling. Therefore, the structure of road networks—the pattern by which roads are connected—can affect the safety of non-motorized road users. To understand the impact of roads’ structural features on pedestrian and bicyclist safety, this study analyzes the associations between road network structure and non-motorist-involved crashes using data from 321 census tracts in Alameda County, California. Average geodesic distance, network betweenness centrality, and an overall clustering coefficient were calculated to quantify the structure of road networks. Three statistical models were developed using the geographically weighted regression (GWR) technique for the three structural factors, in addition to other zonal factors including traffic behavior, land use, transportation facility, and demographic features. The results indicate that longer average geodesic distance, higher network betweenness centrality, and a larger overall clustering coefficient were related to fewer non-motorist-involved accidents. Thus, results suggest that: (1) if a network is more highly centered on major roads, there will be fewer non-motorist-involved crashes; (2) a network with a greater average number of intersections on the shortest path connecting each pair of roads tends to experience fewer crashes involving pedestrians and bicyclists; and (3) the more clustered road networks are into several sub-core networks, the lower the non-motorist crash count. The three structural measurements can reflect the configuration of a network so that it can be used in other network analyses. More information about the types of road network structures that are conducive to non-motorist traffic safety can help to guide the design of new networks and the retrofitting of existing networks. The estimation results of GWR models explain the spatial heterogeneity of correlations between explanatory factors and non-motorist crashes, which can support regional agencies in establishing local safety policies.     

The safe mobility of older drivers: a challenge for urban road designers

For many older adults in most of the Western world, continued mobility (with associated health, well-being, independence and quality of life) means access to a private vehicle, either as a driver or as a passenger. However older driver serious injury and fatality rates per distance travelled are higher than middle-aged drivers, and crash and injury rates are likely to increase in the coming decades as a result of the ageing population, increased car ownership and overall travel amongst older people. Evidence suggests that contributory factors to this high risk are (for most) frailty, (for some) the high proportion of driving in urban areas, and (for a few) reduced fitness to drive as a result of medical conditions and associated functional limitations. Australasia has recently adopted the ‘Safe System’ approach which aims to manage vehicles, the road infrastructure, speeds, and the interactions between these components, to ensure that when crashes do occur, crash energies will remain at levels that minimize the probability of death and serious injury. A system which aspires to achieving Safe System objectives cannot ignore the challenges that older road users pose – and will pose – to the transport system. This paper discusses the so-called ‘older driver problem’, presents crash data and evidence of the impacts of driving location and reduced fitness to drive on crash risk, and recommends countermeasures within the ‘Safe System’ context, with particular reference to urban road design and operation.