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To address the safety concerns generated by truck crashes occurred in big cities, this paper analyzes the zip code tabulation area (ZCTA)-based truck crash frequency across four temporal intervals – morning (6:00–10:00), mid-day (10:00–15:00), afternoon (15:00–19:00), and night (19:00–6:00) in New York City in 2010. A multivariate conditional autoregressive count model is used to recognize both spatial and temporal dependences. The results prove the presence of spatial and temporal dependencies for truck crashes that occurred in neighboring areas. Built environment attributes such as various types of business establishment density and traffic volume for different types of vehicles, which are important factors to consider for crashes occurred in an urban setting, are also examined in the study.

Purpose – The purpose of this chapter is to review the methodological and empirical underpinnings of transport network screening, or management, as it relates to improving road safety. As jurisdictions around the world are charged with transport network management in order to reduce externalities associated with road crashes, identifying potential blackspots or hotspots is an important if not critical function and responsibility of transport agencies.

Methodology – Key references from within the literature are summarised and discussed, along with a discussion of the evolution of thinking around hotspot identification and management. The theoretical developments that correspond with the evolution in thinking are provided, sprinkled with examples along the way.

Findings – Hotspot identification methodologies have evolved considerably over the past 30 or so years, correcting for methodological deficiencies along the way. Despite vast and significant advancements, identifying hotspots remains a reactive approach to managing road safety – relying on crashes to accrue in order to mitigate their occurrence. The most fruitful directions for future research will be in the establishment of reliable relationships between surrogate measures of road safety – such as ‘near misses’ – and actual crashes – so that safety can be proactively managed without the need for crashes to accrue.

Research implications – Research in hotspot identification will continue; however, it is likely to shift over time to both closer to ‘real-time’ crash risk detection and considering safety improvements using surrogate measures of road safety – described in Chapter 17.

Practical implications – There are two types of errors made in hotspot detection – identifying a ‘risky’ site as ‘safe’ and identifying a ‘safe’ site as ‘risky’. In the former case no investments will be made to improve safety, while in the latter case ineffective or inefficient safety improvements could be made. To minimise these errors, transport network safety managers should be applying the current state of the practice methods for hotspot detection. Moreover, transport network safety managers should be eager to transition to proactive methods of network safety management to avoid the need for crashes to occur. While in its infancy, the use of surrogate measures of safety holds significant promise for the future.

Road traffic crashes (RTCs) result in creating significant social and economic hazard for affectees, their families and society. The purpose of this paper is to present studies which were conducted to study the patterns of RTCs in Karachi which is a metropolitan city of Pakistan. The studies were conducted on one of the busiest roads in the city named as Shara-e-Faisal. The influence and contribution of different factors in RTCs has been studied and hazardous road sections of Shara-e-Faisal have been identified. Based on the data analysis, an evaluation model has been suggested to reduce the hazard of RTCs on Shara-e-Faisal. The objective of the presented studies is to increase the present level of safety of road travel by reducing crashes on Shara-e-Faisal.

Existing data of RTCs in Karachi have been analysed for the presented studies. For this purpose, Shara-e-Faisal was divided in sections of 1 km length to study the vehicle crash pattern. Location surveys were conducted to record physical conditions of this road. A cluster analysis was carried out to identify hazardous sections of the road. An evaluation model has been suggested in the end to reduce the hazard of RTCs by identifying hazardous road sections of Shara-e-Faisal.

The analysis of the data revealed that the crashes were higher over weekend and on Monday. Male population, particularly young people, and motorcycle riders were the largest affectees of RTCs. In general, more daytime crashes were recorded as compared to nighttime crashes. The crashes in the mid block of the road and those involving rear-end collisions were higher. The hazardous road locations were related to poor road conditions. Statistical analysis indicated that alternate routes were required to reduce the RTC hazard on Shara-e-Faisal.

The paper is a small, but an original, contribution to identify a potential hazard which is faced by the community in the city. This is the first attempt (to the best of authors’ knowledge) to address the issue of RTCs in Karachi from an engineering view point.

The suggested model can be employed by the authorities as a guideline to mitigate the hazard of road crashes in the country.

The paper provides valuable information on the road traffic incidents, their pattern and contributing factors in one of the largest metropolis of Pakistan. The suggested model can become helpful in reducing RTCs in Pakistan.

Purpose – Information collected from police crash reports has long been the primary source of data for the analysis of factors that determine the likelihood of a crash (crash frequency) and its resulting severity (measured in terms of the extent of injuries to vehicle occupants). Proper cross-sectional analyses techniques, covered in this chapter, are important for guiding safety policy and countermeasures.

Methodology – This chapter provides an overview of some of the more commonly used cross-sectional statistical and econometric methods, and discusses the nuances and their limitations with regard to how they are applied to typical crash-report data.

Findings – The wide variety of analytic methods available to safety researchers makes the selection of appropriate methods critical. This chapter provides important guidance for safety researchers in their choice of methodological approach.

Implications – Understanding the importance of proper model specification, unobserved heterogeneity, endogeneity and other factors covered in this chapter is extremely important in analysing safety data and must be given full consideration before any results are finalised.