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.     

The impacts of congestion on automobile accessibility. What happens in large European cities?

Every day, a significant part of the population in large cities suffers transport congestion. One effect of this is a change in the spatial distribution of accessibility, which may lead to people or businesses finding themselves temporally in areas where accessibility values are lower than either desired or required. This paper studies changes in automobile accessibility over the course of the day, as caused by congestion of the road network in eight metropolitan areas of the European Union: London, Paris, Madrid, Berlin, Barcelona, Rome, Hamburg and Milan. The study was carried out using millions of data points on real speeds on segments of the road networks gathered over the course of two years from TomTom® devices, which provided for the incorporation of a dynamic perspective of accessibility. In each of the areas studied, the different impacts of congestion on automobile accessibility can be observed from differences in the distribution of opportunities and the provision of infrastructures, as well as from differences in culture and customs. Despite these differences, all cities experience two peaks with a lower value during the morning and afternoon. However, results show differences in the intensity and form of the effects of congestion on accessibility in these metropolitan areas. London, Paris and Rome are the cities where congestion has the greatest impact on automobile accessibility, while the Spanish cities are hardly affected by it.     

Built environment and potential job accessibility effects of road pricing: A spatial econometric perspective

This paper analyzes the impacts of the built environment (BE) as it relates to the potential job accessibility (PJA) effects of road pricing. The relationships between the BE elements and PJA under a road charging policy are established using a spatial econometric approach, which uses an integrated land use and transportation model (TRANUS model) and a spatial lag model (SLM). With the intent of further analyzing the differences in the PJA effects of road pricing on traffic analysis zones (TAZs) that contain different combinations of BE elements, a quantitative classification method combining factor and cluster analysis is applied. This will quantitatively categorize TAZs inside and outside the tolled areas. In exploring the relationship between changes in PJA and the road pricing policy, we found the spatial autocorrelation coefficient to be negative. This result suggests that we are unable to increase the PJA of all the regions through road pricing, but rather affect a redistribution of PJA between different regions. Results also indicate that the impacts of road charging on PJA are associated with urban BE elements. Moreover, such effects are the common result of specific characteristics of the BE. The higher the number of jobs, the better the public transportation conditions, and the better the street design (high densities of street and intersections), the less the region will be negatively influenced by a road charging policy, and vice versa. To avoid the negative effects of road pricing on PJA prior to the launch of such a policy, cities should improve public transportation networks and enhance the street design of the road pricing policy areas, especially the toll ring periphery area.     

The effects of area-wide road speed and curvature on traffic casualties in England

Transport provides a range of benefits to society in terms of mobility, access and economic growth. There are however negative impacts of transport, not least in terms of environmental degradation, damage to property, traffic accidents and loss of life. This paper focuses on road traffic accidents, the reduction of which is an important aim of transport policy world wide. The primary objective of this paper is to develop a series of relationships using spatially disaggregated area-level cross-sectional data between different traffic casualties, road traffic speed and road curvature by controlling for other contributing factors associated with area characteristics. The spatial units of the analysis are the 8019 census wards in England. Ward-level casualty data are disaggregated by severity of the casualty (such as fatalities, serious injuries and slight injuries) and by the severity of the casualty related to various road users.The results suggest that increased average speed within a ward is positively associated with total fatalities and serious injuries; and road curvature is found to be negatively associated with road accidents.     

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.     

Socioeconomic differences in public acceptability and car use adaptation towards urban road pricing

Urban road pricing is regarded as an effective instrument to reduce traffic congestion and environmental-related problems in metropolitan areas. Whereas the overall impact of urban road pricing on car use adaptation and public acceptability is known, there are only inconsistent results concerning the socioeconomic differences in the response towards road pricing. However, this knowledge is necessary for the development of urban road pricing packages. This paper uses a segmentation approach to identify groups of car users with a similar background in relevant socioeconomic variables and compares their responses towards road pricing. Three groups are identified: young families, suburban families, and singles and couples. These groups indeed differ in their car use adaptation towards urban road pricing as well as in their preferred revenues use. While all three groups significantly reduced their private car use, the young families reduced their car use most, followed by the group of singles and couples. Complementary measures are discussed that are believed to facilitate car use adaptation of each group in response towards urban road pricing.     

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.     

Willingness to spend and road pricing rates

Using a theoretical model of urban transport system the paper examines the influence of distribution of willingness to spend within the urban population on road pricing rates. It shows that the rates that must be imposed in an urban area in order to maintain pollutant concentration and congestion due to traffic within acceptable levels is heavily dependent on the distribution of the urban population’s willingness to spend. This fact severely limits the reliability of any method for calculating road pricing rates based on theoretical analysis, so that an experimental approach seems necessary. The paper shows that a relation exists between the toll rate per kilometer of trip and the average traffic congestion, which is typical of each urban area and can be determined experimentally by successively imposing three different rates and measuring the corresponding congestion levels. The relation can then be used to determine the pricing scheme when the purpose of road pricing is to maintain both the congestion and the environmental effects due to urban traffic below acceptable thresholds. An example shows how the model can help policymakers in decision-making processes.     

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement.Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.     

Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity

Traffic state in the urban network is a direct reflection of the operational efficiency of the urban transportation system. As the busiest period of the day, traffic states during evening peak hours can effectively measure the capacity and efficiency of the transportation system. The primary objective of this study is to investigate how the potential factors affect traffic states during evening peak hours on weekdays. The geographically weighted regression (GWR) approach was proposed to model the spatial heterogeneity of traffic states and visualize the spatial distributions of parameter estimations. Four types of data including traffic state index (TSI) data, point of interests (POIs) data, road features data, and public transport facilities data were obtained from Shanghai in China to illustrate the procedure. According to the results, the GWR model outperformed the ordinary least square (OLS) model in the explanatory accuracy as well as the goodness of fit. The urban form was revealed to have a significant influence on traffic states and strong local variability for parameter estimations was observed. The number of public and commercial POIs, residential POIs, bus routes, bus stops, the average number of lanes, as well as average traffic volumes can significantly affect the traffic states spatially, and the estimated coefficients of each traffic analysis zone (TAZ) vary across regions. The conclusions of this study may contribute to making the planning and management strategies more efficient for alleviating traffic congestion.     

Spatial disparity of income-weighted accessibility in Brazilian Cities: Application of a Google Maps API

Equity in public transit ridership has attracted the attention of planning authorities as a mechanism to tackle social exclusion. The association of accessibility indexing with different income groups is fundamental to analyses of socio-spatial inequalities and identifying gaps in public transit services. However, few studies have addressed accessibility inequalities in medium-sized cities of the global South. This paper aims to identify spatial gaps in public transit service in seven medium-sized Brazilian cities by analyzing the relative accessibility of public transit and private automobiles for travel to central business districts (CBDs), which are primary employment and service centers. Demographic and socioeconomic data on the seven cities were extracted from the country's 2010 population census. To measure accessibility to CBDs, a Google Maps application programming interface was used to produce realistic estimates of travel times for public transit and private automobiles over different time periods. This method is more accurate than traditional accessibility calculation methods and provides real-time information on traffic conditions, such as speed limits, traffic jams, and waiting times. The study found significant intra-regional differences in accessibility to CBDs by public transit and private automobiles, providing a scientific basis to optimize the socio-spatial distribution of public transit services in seven cities in five different regions of Brazil.     

The dry port concept: connecting container seaports with the hinterland

The dry port concept is based on a seaport directly connected by rail with inland intermodal terminals where containers can be dealt with in the same way as if they were in a seaport. The main purpose of the article is to extend the theory behind the dry port concept and to define three dry port categories; distant, midrange and close. The findings show that the dry port concept can help identify ways of shifting freight volumes from road to more energy efficient traffic modes that are less harmful to the environment, relieve seaport cities from some congestion and facilitate improved logistics solutions for shippers in the port’s hinterland.     

Estimating annual average daily traffic and transport emissions for a national road network: A bottom-up methodology for both nationally-aggregated and spatially-disaggregated results

The regular and robust collection of traffic data for the entire road network in a given country will usually require high-cost investment in traffic surveys and automated traffic counters. This paper provides an alternative and low-cost approach for estimating annual average daily traffic values (AADTs) and the associated transport emissions for all road segments in a country. This is achieved by parsing and processing commonly available information from existing geographical data, census data, traffic data and vehicle fleet data. Ceteris paribus, we find that our annual average daily traffic estimation based on a neural network performs better than traditional regression models, and that the outcomes of our aggregated bottom-up road segment emission estimations are close to the outcomes from top-down models based on total energy consumption in transport. The developed approach can serve as a means of reliably estimating and verifying national road transport emissions, as well as offering a robust means of spatially analysing road transport activity and emissions, so as to support spatial emission inventory compilations, compliance with international environmental agreements, transport simulation modelling and transport planning.     

Spillover effects of the development constraints in London Heathrow Airport

In this article we assess the growth impact of London Heathrow’s development constraints on other airports in the UK. To test the relationship we use a two-stage methodology yielding an estimate of a congestion spillover effect. Our data are passenger traffic from 1990 to 2012 containing both intercontinental and European air traffic. For intercontinental air traffic, our results show high congestion spillover effect between Heathrow and Gatwick airports, and significant but lesser effect to Stansted airport. We also find significant congestion spillover effects from Heathrow to the spatially more distant Manchester and Birmingham airports, showing the extensive spatial impact of Heathrow’s development constraints. For European air traffic, controlling for low-cost air carrier growth, only two airports show significant congestion spillover effects: Gatwick and London City Airports. Illustrating that low-cost carriers do not operate from Heathrow, so its limitations cannot affect the predominant low-cost air traffic in other airports. The novel methodology we present in this paper can be applied to congestion research in general to assess regional and modal spills within networks.     

Negotiating truck arrival times among trucking companies and a container terminal

Congestion of trucks in port areas has created serious environmental and traffic problems in many countries. One of main reasons for congestion is the concentrated arrivals of road trucks at peak hours. A negotiation process for smoothing truck arrivals in peak hours among multiple trucking companies and a terminal is addressed. The negotiation process is to be used for the appointment system for road trucks in container terminals. This paper suggests a mathematical formulation for smoothing the peaks in arrivals considering the inconvenience of trucks from changing their arrival times and the waiting cost of trucks in peak hours. This study proposes a decentralized decision-making model to support the negotiation process between truck companies and the terminal operator. It gives an acceptable solution to every player involved as well as the near optimal solution to the entire system from the global point of view. Numerical experiments are performed to validate the approach in this study.     

A socio-economic assessment of proposed road user charging schemes in Copenhagen

Road pricing, congestion charging, toll-systems and other road charging instruments are intensively discussed in many countries. Although many partial analyses of the consequences have been published, few overall socio-economic analyses have been carried out. The article presents such a socio-economic analysis of four different proposed road pricing schemes for the Copenhagen area. The purpose was to assess all benefits and costs involved, including impacts on traffic and environment, maintenance and financing costs as well as tax distortion effects. It was concluded that the socio-economic surplus of the projects depends crucially on the congestion level. With the current traffic level, road pricing will not yet be socially expedient in Copenhagen. However, if the opening year is postponed to 2015, the two most favourable schemes will turn positive. The analyses also showed that the magnitude of demand response by introducing road pricing is likely to have significant impact on the project surplus. This is an important observation because most short-term driven traffic models will then underestimate the projected surplus. Finally, it was found that the degree to which benefits outweigh costs depends considerably on the use of revenue. Although it may contribute to decreasing road congestion, recycling all of the revenue back to the transport sector turned out to be inefficient and costly.     

Enhancing Park and Ride with access control: A case study of Southampton

Implementing and promoting more sustainable forms of urban transport are key policies of Local Authorities throughout the UK. Park and Ride (P&R) is one such system implemented widely in the UK, especially in historic towns and cities with limited road and parking space in the centre. Some cities (e.g. Rome and London) have also implemented forms of ‘access control’ to reduce congestion and/or pollution in central areas. This paper describes a feasibility analysis of a unique application studied for potential implementation in Southampton—the integration of P&R with access control on a key corridor in Eastern Southampton where traffic demand is likely to increase significantly in the coming years because of new housing developments. The system concept is a P&R facility with express buses to the City centre, keeping the corridor free-flowing for these buses (and other traffic) using a combination of bus lanes and access control.Following an outline of the policy context and system design, this paper then describes the corridor and network modelling undertaken to predict the impacts of the scheme and alternatives of it. This has been based mainly on the CONTRAM dynamic traffic assignment model, which covers the whole of Southampton and its surrounding motorway network. The assessment of the benefits of the various options in this scheme showed that the combination of P&R with signalised access control was the best option to improve the movement of people on the corridor. The paper concludes with a discussion of potential issues for implementation, including the need for complimentary measures and a consistent policy framework.     

Detecting urban road network accessibility problems using taxi GPS data

Urban population growth and economic development have led to the creation of new communities, jobs and services at places where the existing road network might not cover or efficiently handle traffic. This generates isolated pockets of areas which are difficult to reach through the transport system. To address this accessibility problem, we have developed a novel approach to systematically examine the current urban land use and road network conditions as well as to identify poorly connected regions, using GPS data collected from taxis. This method is composed of four major steps. First, city-wide passenger travel demand patterns and travel times are modeled based on GPS trajectories. Upon this model, high density residential regions are then identified, and measures to assess accessibility of each of these places are developed. Next, the regions with the lowest level of accessibility among all the residential areas are detected, and finally the detected regions are further examined and specific transport situations are analyzed.By applying the proposed method to the Chinese city of Harbin, we have identified 20 regions that have the lowest level of accessibility by car among all the identified residential areas. A serious reachability problem to petrol stations has also been discovered, in which drivers from 92.6% of the residential areas have to travel longer than 30 min to refill their cars. Furthermore, the comparison against a baseline model reveals the capacity of the derived measures in accounting for the actual travel routes under divergent traffic conditions. The experimental results demonstrate the potential and effectiveness of the proposed method in detecting car-based accessibility problems, contributing towards the development of urban road networks into a system that has better reachability and more reduced inequity.     

Estimating emissions based on the integration of microscopic traffic simulation and vehicle dynamics model

This study proposes an integrated simulation approach, which consists of a microscopic traffic simulation model, a vehicle dynamics model, and an emission estimation model, in order to estimate emissions based on more reliable vehicle performance measures. The vehicle performance measures such as engine power and engine speed significantly relate to the amount of emissions, and road curvatures and inclinations are the core inputs affecting these vehicle performance measures. Therefore, providing reliable vehicle performance measures reflecting the road geometric attributes is critical for a reliable emission estimation. This study proposes to use the microscopic traffic simulation model for generating vehicle trajectories, which is advantageous in modeling various traffic situations, and the vehicle dynamics model for producing the vehicle performance measures based on the vehicle trajectories. Finally, the outputs from the vehicle dynamics model are fed into the emission estimation model to compute emission measures. This study conducted a case-study using two road sections, one is a hypothesized road section, including various curvatures and inclinations with regular variations, and the other is a Kesselberg road section, which is an actual geometry in Bayern, Germany. The emission measures are estimated in these case-study road sections using both an existing simulation approach and the proposed integrated simulation approach. The difference between these two emission estimation approaches is discussed in terms of the emission measures, including fuel consumption, nitrogen oxides, and particulate matters.     

Do firms flee traffic congestion?

Much transport policy aims to use congestion relief measures to support economic activity, but planners know relatively little about how individual firms respond to traffic congestion. This study helps fill this gap by exploring individual firm location responses to traffic congestion within the Philadelphia metropolitan area between 2003 and 2007. This study tests whether existing, basic-industry firms flee congested areas to minimize exposure to the congestion externality. Relocation responses are estimated and compared for five separate industries (finance and insurance, health care, manufacturing, real estate and leasing, and wholesale trade) using firm-level data collected by InfoUSA and obtained from ESRI. Results suggest that congestion influences firm location decisions, but that the scale of congestion is important. While firms appear to relocate out of areas with high regionally-scaled congestion, areas with high local congestion are associated with a lower likelihood of relocating. In sum, while regional congestion appears to be a drag, local congestion appears to function as an amenity – implying that there is truth in the competing notions among engineers and economists of congestion as a diseconomy and among urban designers of congestion as an amenity.