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 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.     

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.     

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.     

Development of zone system for macro-level traffic safety analysis

Various geographic units have been used in macro-level modeling. Amongst these units, traffic analysis zones (TAZs) have been broadly employed in many macroscopic safety studies. Nevertheless, no studies questioned the validity of TAZs for crash analysis at the macro-level crash modeling. In this study, we point out several possible problems of TAZs as spatial units for macroscopic safety studies. Current TAZs with homogenous crash rates were combined into new single zones. Then we created ten new zonal systems by different zone aggregation levels. The optimal zonal scale for traffic safety analysis zones (TSAZ) was determined using the Brown-Forsythe test. It was found that the zone system with about 1:2 aggregation was the optimal zone system for macroscopic safety modeling. Thus we develop what we call traffic safety analysis zones (TSAZs) that has the potential of reducing several possible problems of TAZs. Also it was shown that TSAZ based models had better fit compared to TAZ based models.     

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.     

Impacts of adverse weather on Arctic road transport

Arctic regions are geographically peripheral and characterized by cold climate, constantly changing weather conditions and strong seasonal variations. This article examines variations in road traffic volume due to adverse weather in an arctic region, a topic that has received little attention in the transportation literature. The subject of the case study is northern Norway's Saltfjellet mountain pass, which is part of European Highway 6 (Ev6). A succinct econometric structural equation model was used to test hypotheses regarding the impacts of fluctuations in temperature, precipitation and wind speed on passenger and freight traffic volumes. The findings indicate that there was some reduction in traffic volume during adverse weather, particularly with respect to passenger traffic. However, the day-to-day variations in traffic volumes were relatively low at the studied section of road, although it is known that generalized transport costs increase significantly in adverse weather due to delays related to poor driving conditions and closed roads. The studied region is rural with limited access to alternative routes or transport modes, thus making this portion of the road particularly important for the communities in the region. Hence, the road users have few other options than using this high-cost road in order to maintain their activities. The use of standardized parameters in transport models to predict the effect of adverse weather on traffic volume, would not be appropriate in the studied context. However, it is recommended that the benefits of reducing the extra costs that adverse weather impose on traffic are estimated and included in road-project assessment tools to capture the burden and strain imposed on road users. This to ensure that appropriate decisions are made regarding the development and improvement of transportation facilities, particularly in rural areas.     

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.     

Transport and rural development in Nigeria

This paper examines the road transport programmes of Nigeria's Directorate of Foods, Roads and Rural Infrastructure (DFRRI) in terms of its expectations and achievements since its inception in 1986 and their general effects on rural development. The analysis shows that, although the Directorate has achieved about 55% of its targeted road programmes, wide variations exist among the states, ranging from 14% to 79%. Nevertheless the construction and/or rehabilitation of about 61 000 kilometres of roads since 1986 has stepped up the production capacity of Nigerian farmers and has opened up several agricultural lands and increased overall accessibility in the country.     

Towards a holistic framework for road safety in Australia

Current road safety programs and thinking in Australia are constructed within a paradigm that tends to accept existing cultural arrangements. Such programs therefore, favour symptomatic solutions and technical and/or physical solutions as a way forward. Fundamental redesign of cultural arrangements is necessary in order to challenge the “culture of speed”. Our research is developing a holistic, social ecological model for reconnecting road safety with communities that value quality of life and slower ways of being. Improving road safety through reduction in the volume and speed of motorised traffic is integrally related to enhancing health and fitness, reducing greenhouse gas emissions, and improving neighbourhood planning and community cohesion. In this regard, community-based travel behaviour change initiatives are deserving of much greater attention in the road safety area. As well as these changes at the personal and community scale, policy changes to urban and transport planning that address the broader issues of sustainability in an era of climate change and peak oil can also be linked to improvements in road safety.     

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.     

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%.     

The geography of freight-related accidents in the era of E-commerce: Evidence from the Los Angeles metropolitan area

Due to the burgeoning demand for freight movement in the era of e-commerce, freight related road safety threats have been growing in both urban and suburban areas, despite the improved general traffic safety over the past decades. The empirical evidence on how freight trucks related crashes are distributed across neighborhoods and correlated to spatially varying factors is, however, highly limited. This article uses data from the Los Angeles region in 2018 to analyze the spatial patterns of freight trucks related traffic crashes and examines the major factors that contribute to those patterns using spatial econometric models. Maps show that freight trucks related crashes are highly associated with major freight generators but less clustered than the overall traffic crashes. Results from the spatial Durbin model indicate that access to freight generators, economic attributes, land uses, road infrastructure, and road network variables all contribute to the spatial distribution of freight trucks related crashes. The findings could help transport planners understand the dynamics of freight trucks related traffic safety and develop operational measures for mitigating the impacts of growing goods movement on local communities.     

Hierarchical system of road networks with inward, outward, and through traffic

This paper formulates a continuous model of a grid road network for determining the optimal hierarchical system of road networks. The hierarchical system is represented as the proportions of area taken up by roads at each level of the hierarchy. The objective is to derive the optimal ratio of road areas that minimizes the total travel time. The model explicitly incorporates inward, outward, and through traffic as well as inner traffic to examine how traffic composition affects the optimal ratio of road areas. The result reveals that the optimal ratio of major arterial roads increases with inward, outward and through traffic.     

Annual average daily traffic estimation in England and Wales: An application of clustering and regression modelling

Collection of Annual Average Daily Traffic (AADT) is of major importance for a number of applications in road transport urban and environmental studies. However, traffic measurements are undertaken only for a part of the road network with minor roads usually excluded. This paper suggests a methodology to estimate AADT in England and Wales applicable across the full road network, so that traffic for both major and minor roads can be approximated. This is achieved by consolidating clustering and regression modelling and using a comprehensive set of variables related to roadway, socioeconomic and land use characteristics. The methodological output reveals traffic patterns across urban and rural areas as well as produces accurate results for all road classes. Support Vector Regression (SVR) and Random Forest (RF) are found to outperform the traditional Linear Regression, although the findings suggest that data clustering is key for significant reduction in prediction errors.     

Why standard modelling and evaluation procedures are inadequate for assessing traffic congestion measures

As traffic congestion in Hong Kong worsens with the growing use of motor vehicles, to determine what has to be done to achieve and maintain an acceptable level of mobility for persons and goods becomes the subject of intense debate by economists, planners and politicians. Although many traffic congestion measures have been suggested and investigated, they are mainly confined to expansion of transport infrastructure and measures to make effective use of roads. New roads and public transport improvements may encourage increases in traffic and changes in the patterns of trips, while traffic management and regulation measures may in turn lead to suppression in road traffic. In order to evaluate these measures, it is required to make use of a transport model in assessing their traffic impacts. However, there are always discrepancies between the traffic forecasts and the actual flows on the roads. This paper investigates why the standard modelling and evaluation procedures currently used by the Hong Kong Government are inadequate for assessing the traffic congestion measures. Empirical evidence is given together with discussion on modelling and evaluation issues raised by the existence of suppressed/induced traffic.     

Modeling AADT on local functionally classified roads using land use,road density,and nearest nonlocal road data

The focus of this research is to model the influence of road, socioeconomic, and land-use characteristics on local road annual average daily traffic (AADT) and assess the model's predictability in non-covered location AADT estimation. Traditional ordinary least square (OLS) regression and geographically weighted regression (GWR) methods were explored to estimate AADT on local roads. Ten spatially distributed counties were considered for county-level analysis and modeling. The results indicate that road density, AADT at the nearest nonlocal road, and land use variables have a significant influence on local road AADT. The GWR model is found to be better at estimating the AADT than the OLS regression model. The developed county-level models were used for estimating AADT at non-covered locations in each county. The methodology, findings, and the AADT estimates at non-covered locations can be used to plan, design, build, and maintain the local roads in addition to meeting reporting requirements. The prediction error is found to be higher at urban areas and in counties with a smaller number of local road traffic count stations. Recommendations are made to account for influencing factors and enhance the local road count-based AADT sampling methodology.     

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.     

A multi-criteria analysis framework including environmental and health impacts for evaluating traffic calming measures at the road network level

Recent interests in both vehicle emissions and public health have facilitated the development of more eco-friendly transportation systems. This study developed a multi-criteria evaluation framework to evaluate the effectiveness of traffic calming measures (TCMs) from the various perspectives at the road network level; operational efficiency, traffic safety, environmental and health impacts. The proposed methodology employs four-step sequential simulation experiments, including driving, traffic flow, emissions, and air dispersion simulations. The results obtained from these four simulations are used to evaluate the effectiveness in terms of safety and operational efficiency in addition to environmental and health impacts. A multi-criteria value function based on the weights estimated from the analysis of an analytical hierarchy process (AHP) is applied in the evaluation framework. As an application, chicanes and speed humps widely implemented in Korean school zones were evaluated at the road network level. The proposed simulation-based approach is expected to be effectively used for the decision-making process in selecting better alternatives for TCM.