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.     

Goods movement,road safety,and spatial inequity: Evaluating freight-related crashes in low-income or minority neighborhoods

The significant growth in freight traffic and relevant crashes has aroused increasing concerns about road safety threats in local communities. We use data from the Minneapolis-St. Paul Metropolitan Area and examine the spatial relationship between freight-related crashes and neighborhoods with low-income and minority populations. We find that both household income and percentage of minority population are significantly correlated with the density of both freight-related crashes and freight-related crashes causing severe injuries and fatalities. The results indicate that freight-related crashes are subject to a spatial inequity problem. The findings underscore the importance of incorporating freight-related safety improvement within these low-income and minority neighborhoods.     

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.     

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.     

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.     

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.     

Context sensitive multimodal road planning: a case study in Cape Town, South Africa

Road planning practice relies almost exclusively on parameters related to traffic factors, such as private vehicle speeds and volumes. In many developing countries the requirements for public transport and non-motorised transport are not explicitly integrated into the planning process, despite the fact that these form the primary mode of transport for the majority of the population. This affects the mobility opportunities for these sectors of the population and contributes to poor road safety, especially with regards to pedestrians. The research outlined in the paper posits that, in order to assess the usage and needs of the road holistically, other factors related to the adjacent land uses, socio-economic characteristics of the population the road serves, and the environmental context within which the road is located, factors heavily in how the road is used and should, therefore, be considered within the planning process. The paper describes a methodology to include these factors in the planning of roads. The method attempts to prioritise amongst the five primary road based modes (public transport, car, freight, walking and cycling) based upon a combination of traffic and non-traffic factors. The method employed uses a geographic information system (GIS) based spatial multiple criteria evaluation (SMCE) model with inputs from widely available data sources such as census, household travel surveys, land use and environmental data to arrive at solutions for modal priorities. A case study is conducted along an arterial route in Cape Town, South Africa, providing infrastructure planning recommendations and audit possibilities for the future. Since weighting is an important driver in the SMCE process, a sensitivity analysis is conducted to investigate the effect of alternative weighting schemes on the outputs from the method.     

Exploring the relationship between truck load capacity and traffic accidents in the European Union

Applying econometric techniques to EU28 panel data and controlling for explanatory variables such as road types, we find that increased truck load capacity does not necessarily aggravate road traffic safety. Specifically, heavy trucks do not seem to be linked with greater numbers of traffic fatalities/accidents, medium trucks appear to be the worst performers in terms of fatalities, and light trucks seem to be the worst for accidents. In summary, our results clarify the complex relationship between truck load capacity and road safety, pointing to the existence of a negative correlation for accidents per capita and an inverse U-shaped curve for fatalities per capita.     

Driver behaviour,truck motion and dangerous road locations – Unfolding from emergency braking data

Road vehicles equipped with measurement, computing, data storage and data communication capabilities can be utilised as probe-vehicles. Data received from such vehicles can provide valuable traffic and traffic safety information in respect of the covered routes and the connecting road network. In this study, trucks negotiating their normal daily haulage trips were used as probe-vehicles and the data recording their vehicular emergency events, such as abrupt braking events, detected by their on-board vehicular safety systems were analysed. The motivation for such an analysis is manifold. The aspect emphasized here is that the recorded vehicular emergency actions and events can be seen as surrogate safety events. Some of these surrogate safety events mark traffic incidents and therefore can be used as input by the surrogate safety assessment methodology. Though the vehicular emergency data used herein provides only sparse spatial and temporal coverage of the road network investigated, its analysis led to some interesting findings about interactions between drivers, trucks and roads.     

Decomposing the decoupling of Danish road freight traffic growth and economic growth

In recent years many European countries have seen a decoupling of the growth in road freight traffic (vehicle kilometres) from economic growth. A similar decoupling has not been observed in road freight transport (tonne kilometres). In this paper the historical growth in national Danish road freight traffic and transport is attributed to causes using a Divisia index decomposition method. It is demonstrated that overall road freight traffic growth is a consequence of often opposite pointing growth effects in the underlying factors. The observed decoupling of road freight traffic growth from economic growth is mainly the result of use of larger vehicles, increasing average loads, and less empty running. Growth in road freight transport is primarily caused by growth in production. A decrease in the number of tons lifted per tonne produced (the handling factor) is offset by an increase in the tonne kilometres per tonne lifted.     

A time-period choice model for road freight transport in Flanders based on stated preference data

This paper presents one of the first models explaining the choice of time-period in road freight transport. Policies that would shift some fraction of the trucks from peak to earlier and later periods will contribute to the reduction of congestion. Therefore there is an increasing interest in modelling the time-period sensitivity of road freight transport to changes in travel time and cost by period. The model developed here is based on a stated preference survey amongst receivers of goods in Flanders and was implemented in the strategic freight transport model of the Flemish authorities.     

Macro and micro models for zonal crash prediction with application in hot zones identification

Zonal crash prediction has been one of the most prevalent topics in recent traffic safety research. Typically, zonal safety level is evaluated by relating aggregated crash statistics at a certain spatial scale to various macroscopic factors. Another potential solution is from the micro level perspective, in which zonal crash frequency is estimated by summing up the expected crashes of all the road entities located within the zones of interest. This study intended to compare these two types of zonal crash prediction models. The macro-level Bayesian spatial model with conditional autoregressive prior and the micro-level Bayesian spatial joint model were developed and empirically evaluated, respectively. An integrated hot zone identification approach was then proposed to exploit the merits of separate macro and micro screening results. The research was based on a three-year dataset of an urban road network in Hillsborough County, Florida, U.S.Results revealed that the micro-level model has better overall fit and predictive performance, provides better insights about the micro factors that closely contribute to crash occurrence, and leads to more direct countermeasures. Whereas the macro-level crash analysis has the advantage of requirement of less detailed data, providing additional instructions for non-traffic engineering issues, as well as serving as an indispensable tool in incorporating safety considerations into long term transportation planning. Based on the proposed integrated screening approach, specific treatment strategies could be proposed to different screening categories. The present study is expected to provide an explicit template towards the application of either technique appropriately.     

Monitoring trucks to reveal Belgian geographical structures and dynamics: From GPS traces to spatial interactions

Despite the fact that freight transport has a huge impact on the economy and the environment, Belgian datasets have always been scarce or restricted to very small a-spatial samples. Spatial data collected in Belgium for toll-paying trucks are here examined, and geographical structures and dynamics are extracted from this massive dataset. The originality of this dataset is its exhaustivity and its real-time approach: the location of all the trucks circulating in Belgium is collected every 30 s.The paper first relates to the methodology applied when using and transforming big data generated by On Board Units GNSS (cleaning, transforming and pre-processing). Second, it maps and comments on the movements (traffic) and stops of trucks within the whole country, providing a clear picture of the Belgian situation, useful for regional planners and logistics companies. Finally, the flows of trucks observed between Belgian locations enable the country to be divided into mathematical communities of places that interact the most. Analyses are performed for sub-categories based on the country of registration, underlining the spatial specificities of freight transit in Belgium. This exploratory spatial data analysis enables to reveal not only multi-level spatial structures associated with urban hierarchies and the transport infrastructure, but also firm locations or political organizations and to consider the complexity and interconnectivity of any measure taken for a more sustainable future. With a clear methodological framework to cope with the data pre-processing, this paper opens the way to various potential applications linked with freight transportation in Belgium.     

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.     

A multi-level spatial-temporal model for freight movement: The case of manufactured goods flows on the U.S. highway networks

The spatial production, attraction, and movement of manufactured goods are vital to the economy of a region and country. The U.S. department of transportation also mandates to incorporate continuing and efficient freight movement and infrastructure into statewide and local long range planning. Studies on supply, demand, and transport of manufactured goods by firm, industry, mode, or commodity are vast in the logistics and supply chain literature. However, relatively sparse research is available on aggregated movement or freight on national, state, or local transportation networks. Better understanding and modeling freight movement on highway networks to facilitate local transportation, land use, economic development, and comprehensive planning is at the heart of freight research. Therefore, the major endeavors and novel contributions of this research include a conceptual framework proposed for freight movement research, a multi-level spatial-temporal freight model based upon the social optimum assignment for optimal “from”, “to”, “within”, and “through” freight flows of manufactured goods on the U.S. highway networks, and a set of performance measures designed to reveal states in terms of their competitive advantages in production, attraction, self-sufficiency, or cross-road. The freight flows were first visualized and highlighted by state at the U.S. level, then by county at the state level for Oklahoma, and finally by traffic analysis zone for the Tulsa metropolitan area. The spatial split of freight flows was accomplished through using freight, network, and demographic-economic databases at state, county, and zone scales.     

Safety classification using GIS in decision-making process to define priority road interventions

Given the current economic situation of the Portuguese municipalities, it is necessary to identify the priority investments in order to achieve a more efficient financial management. The classification of the road network of the municipality according to the occurrence of traffic accidents is fundamental to set priorities for road interventions. This paper presents a model for road network classification based on traffic accidents integrated in a geographic information system. Its practical application was developed through a case study in the municipality of Barcelos. An equation was defined to obtain a road safety index through the combination of the following indicators: severity, property damage only and accident costs. In addition to the road network classification, the application of the model allows to analyze the spatial coverage of accidents in order to determine the centrality and dispersion of the locations with the highest incidence of road accidents. This analysis can be further refined according to the nature of the accidents namely in collision, runoff and pedestrian crashes.     

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%.     

Land development implications of the British rail freight renaissance

The post-war era saw a dramatic decline in both the volume and market share of freight transported by rail in Britain. This decline was associated with the closure of thousands of rail freight facilities and reduction by one third in the route mileage of the national railway network. However, as a result of railway privatisation and increasing constraints on the competitiveness of the road haulage industry, the long decline has halted and rail freight traffic is growing. This article reviews the implications of this change for the demand for rail freight facilities and for extra capacity in the railway network, and considers how the land-use planning and transport planning systems might respond.     

On structural inelasticity of modal substitution in freight transport

At the European level there is an increasing focus on how freight transport can be moved from trucks on roads to more environmentally friendly modes such as rail and ship. A large proportion of the transport services between OD pairs, however, cannot be substituted since there is only one alternative available. The paper investigates the magnitude of this “structural inelasticity” of modal substitution in freight transport due to a sparser layout of rail and ship-based freight networks compared to road. In the analysis we use a recent Scandinavian freight demand model covering more than 800 zones. We find that the structural inelasticity is very significant - in particular for transportation over less than 500 km. Moreover, the inelasticity varies greatly with commodity groups and between OD pairs, and it depends strongly on the port and rail infrastructure. The results suggest that pure charging instruments (road pricing for trucks) in many regions will have limited mode substitution impacts. However, if combined with structural changes in terms of improved infrastructure for rail and ship, impacts may be greater.     

The impact of the ‘school run’ on road traffic accidents: A spatio-temporal analysis

Engineering and improved road safety education has resulted in an overall decrease in road traffic accident numbers in Christchurch, New Zealand. The temporal trends of crash data from 1980 to 2004 reveal that lowering of crash rates is not occurring at a uniform rate throughout the day, with comparative increases in crash rates occurring during morning rush hour, and during the ’school run’. No spatial clustering around schools was identified. This suggests that policies to reduce school travel related road accidents need to be focused on reducing overall traffic levels rather than focusing geographically on areas in the immediate vicinity of schools.