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.     

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.     

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.     

Taking another look at cycling demand modeling: A comparison between two cities in Canada and New Zealand

Cities around the world are investing in their cycle network as a way to reduce traffic congestions and improve population health. For effective infrastructure investment decision-making, it is critical to understand the factors affecting cycling demand. This study investigates the combined effects of spatial and temporal factors on the demand of cycling in Auckland, New Zealand and Kelowna, Canada. A latent segmentation-based negative binomial (LSNB) model is developed utilizing daily cycling counts over a year from Auckland and Kelowna. The LSNB model captures unobserved heterogeneity based on the temporal attributes, and land use and neighborhood characteristics of the cycling facilities. The models confirm the influence of weather, bicycle facility type, built environment, traffic, land use, and accessibility attributes. The model results suggest that higher temperature, lower rainfall, increased length of shared paths, lower AADT, and closer distance to bus stop are likely to increase cycling demand in Auckland. The model also confirms heterogeneity. For example, road-connectivity index and bike index vary across the urban and suburban areas of Auckland. For Kelowna, higher temperature, lower rainfall, lower snowfall, and closer distance to water bodies are likely to increase cycling demand. In the case of heterogeneity, the effects of road-connectivity index, bike index, and AADT varies over the urban and suburban areas of Kelowna. Analysis suggests that increasing bike index is one of the critical factors to increase cycling demand in the suburban areas of Auckland and Kelowna.     

Planning for an unpredictable future: Transport in Great Britain in 2030

This paper describes a research study, which explores alternative future scenarios for Great Britain in the year 2030 and the implications these have for travel demand and transport provision. Five alternative future scenarios are represented in the GB national transport model and forecasts are obtained for trip making, traffic levels, congestion and emissions in 2030. For all scenarios it is expected that there will be significant traffic growth. Traffic growth is restricted most in scenarios including distance-based road charging on motorways and trunk roads. However, congestion and carbon dioxide emissions are most effectively limited in scenarios with congestion-based road charging, major improvements to urban public transport and investment in new fuel technologies and in improving engine efficiency.     

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.     

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.     

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 new zone system to analyze the spatial relationships between the built environment and traffic safety

This study aims to investigate the impacts of the built environment on traffic safety at a zonal level using a newly developed crash-related zone system. Traffic analysis zones (TAZs) have been widely employed to analyze traffic safety at a macroscopic level. However, this zone system use may present problems. Unlike previous studies, in which new zoning systems were created from aggregating TAZs, in this study the new zone system, formed by traffic safety analysis zones (TSAZs), is created from the smallest available census units. Geographically Weighted Negative Binomial Regression (GWNBR) models are used and a comparative analysis between non-spatial global crash prediction models and spatial local GWPR (Geographically Weighted Poisson Regression) and GWNBR models using the two zonal systems is presented. We find that TSAZs based models performed better than TAZs based models, especially when combined to the GWNBR technique. Our results show that several features of the built environment are significant crash predictors, and that the relationships among these features and traffic safety vary across space. By combining a crash-related zonal system with spatial GWNBR models to understand the built environment effects on traffic safety, the results of the analysis can help urban planners to consider traffic safety proactively when planning or retrofitting urban areas.     

网络拓扑结构对交通流量分布特性的影响分析

分析城市交通网络特性,建立双层城市交通网络模型,其中下层为道路网络,上层为出行网络.以节点平均负荷、拥堵节点、网络负荷标准差、基尼系数为主要指标,研究居民出行网络拓扑结构变化过程中道路网络流量分布的变化情况.研究结果显示,出行网络拓扑结构对交通量分布有较大影响,且存在最优出行网络结构,使城市交通状况达到最佳.     

Transportation and recreation: a case study of visitors driving for pleasure at Acadia National Park

Automobiles and roads are as much of a way of experiencing national parks as they are a means of conveyance. This study examines experiential aspects of transportation on Acadia National Park’s primary scenic road – Ocean Drive. Interviews with vehicle-based road users were conducted to identify indicators to measure and manage experiential quality on Ocean Drive. Also, a survey was conducted to make comparisons with important variables identified on “transportation-only” urban roads. Results suggest that (1) Ocean Drive is important to park visitors’ experience; (2) experiential indicators for Ocean Drive include vehicle crowding, scenery, and travel freedom/convenience; and (3) experiential aspects of transportation on scenic roads in parks may differ substantially from urban roads. Study findings suggest a need to deliberately and thoughtfully plan and manage for quality recreational experiences on roads in national parks and related areas.     

Discovering temporal and spatial patterns and characteristics of pavement distress condition data on major corridors in New Mexico

Roadway networks, as part of transportation infrastructure, play an indispensable role in regional economies and community development. The high-quality pavement serviceability of these networks is essential to ensure safe, cost-effective daily traffic operations. In-depth analyses of network-wide pavement surface condition data are necessary inputs for optimal pavement design and maintenance, traffic safety enhancement, and sustainable traffic infrastructure system development. This study aims to investigate various pavement distress condition performance measurements and their correlations to better understand temporal–spatial characteristics of roadway distress based on pavement distress condition data collected in New Mexico from 2006 to 2009. Eight major corridors across various urban and rural areas were selected for analyzing pavement surface-distress conditions and discovering their intrinsic characteristics and patterns across both temporal and spatial domains. The results show that there are not strong correlations among different distress measurements, implying the rationality of the current pavement performance measurement protocol used by the state transportation agencies. Regression models were established and GIS-based spatial analyses were performed to extract temporal and spatial patterns of Distress Rate (DR) data. The model results illustrate significant correlations of the DR data on the same route between two consecutive years, which can be partially characterized by a Markov process. GIS-based spatial investigations also show unique features of pavement condition deterioration attributed to diverse geometric characteristics and traffic conditions, such as vehicle compositions and volumes and urban and rural areas. The research findings are helpful to understand the characteristics of pavement distress conditions more clearly and to optimize traffic infrastructure design and maintenance.     

From colonization to national territories in continental West Africa: the historical geography of a transport infrastructure network

This paper argues that in order to study the geohistory of a transport infrastructure network it is necessary to identify the link between networks and territorial scales during a given period. It focuses on the creation of road and rail networks in French West Africa during the colonial period and in the land-locked States during the national period, which involves post-independence development policies. The network is perceived in this paper as the manifestation of the political will to facilitate traffic within a delimited space. Within the colonial and then the national periods roads and railways were thus constructed as part of a process that can be interpreted as the outcome of a compromise between a territorial project and the capacity to implement it. This geohistorical study thus allows us to identify, with reference to the link between borders and networks, the successive geographical linkages (railway and port combinations with their subsequent branches, national roads, economic roads, external connections) which have been applied in the delimited spaces in question. This exercise has concentrated particularly on continental West Africa.     

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.     

Infrastructure development for alternative fuel vehicles on a highway road system

A new mathematical model for positioning alternative fuel (AF) refueling stations on directed-transportation networks with the objective of maximizing the coverage of path flow volume is proposed. This model is especially designed for developing an AF infrastructure on toll roads and other highways, where vehicles do not need to exit the road network for refueling, some candidate station locations are not located at interchanges, and some stations may only service vehicles on one driving direction. The proposed model is applied to the Pennsylvania Turnpike System using the 2011 truck traffic data and considering different vehicle driving ranges.     

Regional road development,rural and urban poverty: Evidence from China

The study estimates the impact of road investments on overall economic growth, rural and urban growth, and rural and urban poverty reduction. To achieve these goals, an econometric model that captures the different channels through which road investment impacts growth and poverty is developed and estimated using provincial-level data for 1982–1999 in China. Low-grade (mostly rural) roads have benefit/cost ratios for national GDP that are about four times larger than the benefit/cost ratios for high-grade roads. In terms of poverty reduction, low-grade roads raise far more rural and urban poor above the poverty line per yuan invested than do high-grade roads. Another significant finding of the study is the trade-off between growth and poverty reduction when investing in different parts of China. Road investments yield their highest economic returns in the eastern and central regions of China, while their contributions to poverty reduction are greatest in western China (especially the southwest region).     

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.     

The role of road safety in a sustainable urban mobility: An econometric assessment of the Spanish NUTS-3 case

There has been a structural change in mobility in major Spanish cities in recent decades, with a switch to the pattern followed in other countries in the area. A shift has taken place from a traditional Mediterranean model to a North American city stereotype, with uncontrolled motorization and major implications for public health. This article specifically analyzes negative road safety-related externalities that result from this process, given that the trend seems to show a steady decline in road safety accidents on urban roads in Spain, with major differences among NUTS-3 provinces. The objective is to evaluate the factors that empirically explain these differences for the 2003–2013 period using a panel data analysis. Results show that a key role is played by urban development variables, such as population density and improvements in health services, with advances linked to more accessible and sustainable urban transportation, such as the Smart City concept. Not only does this article close a gap in the literature, but the findings can also serve as a practical guide for the development and implementation of urban mobility and road safety plans, and reveals the special needs of the most vulnerable groups.     

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.