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.     

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.     

Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression

Geographically weighted regression (GWR) models have been employed in previous studies regarding vehicular travel demands, but few studies have locally modeled walking travel demands at intersections to address the issue of spatially varying relationships. Harnessing a comprehensive collection of walking and bicycling traffic counts over 10 years in Chittenden County, Vermont, US, along with socioeconomic characteristics, transit accessibility indices, land use attributes and characteristics of intersections and roadway networks, this study utilizes GWR models to identify whether there are spatially varying relationships between active mode travel demands and ambient built-environment attributes. One Ordinary Least Square (OLS) model and two GWR models were parametrically calibrated: a full GWR model of all local variables and a mixed GWR model of both global and local variables. K-fold cross-validation method is used to select variables that significantly influence the volume of active travel modes in the OLS model. The uniform set of variables is investigated in two GWR models. Only residuals of the mixed GWR model exhibit spatial independence. The prediction accuracy of the three models is respectively compared by means of the k-fold cross-validation method. Results show that the mixed GWR model has higher prediction accuracy, while the other two models have roughly the same level of performance. We find that not all independent variables possess a spatially varying relationship with active mode volumes. The flexibility of the mixed GWR model that allows some independent variables to be global strengthens its prediction power. With these findings, transportation planners can dynamically estimate bicycle and pedestrian volumes at widespread intersections, and this geographical realism would facilitate local transportation planning, facility design, safety enhancement and operation analysis, as well as instilling new insights into interdisciplinary spatial research domain.     

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.     

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.     

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.     

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.     

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.     

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.     

An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai,China

Revealing dockless bike-sharing utilization pattern and its explanatory factors are essential for urban planners and operators to improve the utilization and turnover of public bikes. This study explores the dockless bike-sharing utilization pattern from the perspective of bike using GPS-based bike origin-destination data collected in Shanghai, China. In this paper, utilization patterns are captured by decoupling several spatially cohesive regions with intensive bike use via non-negative matrix factorization. We then measure the utilization efficiency of bikes within each sub-region by calculating Time to booking (ToB) for each bike and explore how the built environment and social-demographic characteristics influence the bike-sharing utilization with ordinary least squares (OLS) regression and geographically weighted regression (GWR) models. The matrix factorization results indicate that the shared bikes mainly serve a certain area instead of the whole city. In addition, the GWR model shows higher explanatory power (Adjusted R² = 0.774) than the OLS regression model (Adjusted R² = 0.520), which suggests a close relationship between bike-sharing utilization and the selected explanatory variables. The coefficients of the GWR model reveal the spatial variations of the linkage between bike-sharing utilization and its explanatory factors across the study area. This study can shed light on understanding the demand and supply of shared bikes for rebalancing and provide support for operators to improve the dockless bike-sharing utilization efficiency.     

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.     

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.     

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.     

Adaptive nature of coordinated road traffic injury prevention responses in some OECD countries

This study examines the context of coordinated responses, triggers for coordinated responses, and preference for or choice of coordinating strategies in road traffic injury prevention at a local level in some OECD countries. This aim is achieved through a mixed-methodology. In this respect, 22 semi-structured interviews were conducted with road traffic injury prevention experts from five OECD countries. In addition, 31 professional road traffic injury prevention stakeholders from seven OECD nations completed a self-administered, online survey. It found that there was resource limitation and inter-dependence across actors within the context of road traffic injury prevention at a local level. Furthermore, this study unveiled the realization of resource-dependency as a trigger for coordinated responses at a local level. Moreover, the present examination has revealed two coordinating strategies favored by experts in road traffic injury prevention – i.e. self-organizing community groups, which are deemed to have a platform to deliver programs within communities, and the funding of community groups to forge partnerships. However, the present study did not appear to endorse other strategies such as the formalization of coordinated responses or a legal mandate to coordinate responses.In essence, this study appears to suggest a need to manage coordinated responses from an adaptive perspective with interactions across road traffic injury prevention programs being forged on a mutual understanding of inter-dependency arising out of resource scarcity. In fact, the role of legislation and top-down national models in local level management of coordinated responses is likely to be one of identifying opportunities to interact with self-organized community groups and fund partnership-based road traffic injury prevention events.     

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.     

The impacts of road tolling: A review of Norwegian experience

Road tolling has been used extensively for funding of national roads in Norway since 1982. The article, which is primarily based on review of literature, identifies the impacts of tolling in four key policy areas: economic efficiency, regional development, regional redistribution and democracy. Toll financing may change the ranking of projects but does hardly reduce construction costs. The recurrent budget has been quite stable and toll revenues therefore represent net additional resources for road investment. Regionally, tolling has led to a shift of road investments to more central areas. Tolling has increased local political influence compared to national influence.     

Policy transfer and the introduction of road pricing in Valletta,Malta

Charging motorists to pay for driving at times and/or in places where there is insufficient capacity to meet the level of demand has long been recognised by transport economists as an efficient way to address traffic congestion. However, only a few locations have actually decided to use this mechanism, with Valletta one of the more recent cities to introduce such a scheme. This paper aims to present a case study of road pricing in Valletta and the role played by policy transfer in its introduction. To do this, it applies data gathered from existing literature, in-depth interviews and direct observation with those involved in the policy development. Experience of road pricing was gained from London, Durham, Edinburgh and Stockholm. One of the major barriers to policy transfer was felt to be the unique geography of the island however international events, local conditions and a political champion driving change were critical factors to the successful implementation of the road pricing scheme. Policy transfer was deemed useful in terms of policy development, relations with stakeholders, scheme design and administrative approaches. This paper concludes by identifying the contributions of the case study of Valletta to the policy transfer literature.     

The factors in residents' mobility in rural towns of China: Car ownership,road infrastructure and public transport services

The improvement of rural people's mobility in developing countries has informed many policies. Still, debates remain on which policies are efficient, for instance, building more roads, providing public transport or promoting car ownership. The empirical evidence for these debates at the national level remains scarce. As a result, this paper aims to provide fresh evidence for discussions by examining residents' mobility in China using nationwide survey data with 12,524 respondents from 119 rural towns. The results of the analysis show car ownership is the most significant factor influencing rural people's mobility than other factors. Higher car ownership relates to a higher travel frequency to counties or cities. Other kinds of transport vehicles (i.e. electric cars, motorcycles and electric bikes) also have positive but relatively weaker impacts on rural mobility. For public transport, it is more accessible to access bus stops, which encourages travel to higher-order centres rather than increasing the frequency of county bus services. The accessibility of high-quality road systems tends to have a negative influence and has combined effects with levels of local services. People from towns with insufficient local services and poor access to highways travel the most frequently to higher-order centres. This study highlights the critical role of road investments and car ownership enhancement policies in improving mobility. Moreover, this study also underscores the supplemental role of public transport services given the current low car ownership rates in rural towns of China and the global consensus on sustainable green transport development. It highlights the importance of engaging eco-friendly and locally adaptive transport alternatives, such as electric cars and electric bikes. It also calls for a rational distribution of bus stops and more flexible, convenient, and physically accessible public transport and carshare modes in rural China.     

Toward a spatial understanding of active transportation potential among a university population

Universities and surrounding communities stand to benefit when active travel mode choices are elevated. Despite this, there is little research on travel mode choice at commuter universities and, in particular, the nonlinear spatial relationships among active travel potential and various contextual and compositional factors. The purpose of this study was to examine and visualize linkages among personal, household, density, diversity, and design factors, and active travel (bicycling, walking, and mass-transit modes) among a commuter-university population residing throughout southeastern Michigan, USA. This was accomplished by employing exploratory spatial data analysis (ESDA), ordinary least squares (OLS) regression, and a geographically weighted regression (GWR) model. The GWR model outperformed the traditional OLS model in terms of goodness of fit (R² = .534 and R² = .461, respectively). A novel cartographic mapping technique was employed to depict where statistically significant parameter estimates negatively or positively influenced active travel. The main finding was that personal, household, density, diversity, and design estimates varied in both magnitude and spatiality throughout the university's study area. Interestingly, distance was not a universal barrier to active travel potential. These variations emphasize the importance of promoting active transportation through localized interventions as well as coordinating efforts among universities and surrounding communities.     

From roadkill to road ecology: A review of the ecological effects of roads

Transportation infrastructure affects the structure of ecosystems, the dynamics of ecosystem function, and has direct effects on ecosystem components, including their species composition. Clearly, the construction of transport lines results in the direct destruction and removal of existing ecosystems, and the reconfiguration of local landforms. However, transportation systems, and more specifically, roads, have a wide variety of primary, or direct, ecological effects as well as secondary, or indirect, ecological effects on the landscapes that they penetrate. The effects of roads can be measured in both abiotic and biotic components of terrestrial and aquatic ecosystems. The nature of road systems as network structures renders vast areas of the landscape as road-affected, with small patches of isolated habitat remaining beyond the ecological influence of roads. The increasing attention of scientists to the unintended ecological effects of roads has resulted in the emergence of the science of “Road Ecology,” marked with the publication of a multi-authored volume, Road Ecology: Science and Solutions, in 2003.