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.     

Logistics facilities for intra and inter-regional shipping: Spatial distributions,location choice factors,and externality

Existing studies of urban logistics facility locations and their impacts on negative externality tend to focus on the movements of freight that start and end within a metropolitan area and overlook the role of the shipments with origin or destination outside of the urban area – external shipments – in characterizing logistics facilities. This research aims to fill such gap. Focusing on the Tokyo Metropolitan Area, we analyze locations and goods vehicle trips associated with four logistics facility groups that have different splits between intra and inter-regional trips for inbound and outbound shipments. The spatial density analysis and the location choice modeling highlight the differences in the distribution and location choice factors among those groups. The importance of trip distance minimization is more evident for the facilities that serve intra-regional shipments. Furthermore, we show that the traffic impacts of goods vehicle traffic associated with logistics facilities also differ in many ways among the above-mentioned groups. Finally, we discuss the policy insights to address their heterogeneity in the contribution to externality.     

Correlation analysis of day-to-day origin-destination flows and traffic volumes in urban networks

The trip patterns on an urban network can be represented by two main variables: origin-destination flows (OD flows), defined as the number of trips between two locations over a given time period, and traffic volumes, defined as the number of vehicles that cross a street over a given time interval. Past research on the dynamic of traffic assignment and OD estimation suggested that the traveler's decisions vary on a day-to-day basis and that their most recent decisions may affect their current travel decisions. Based on these assumptions, this study analyzed the autocorrelation of a set of day-to-day series of traffic volumes and OD flows generated from a large collection of traffic sensors, identifying the data's correlation structure over different locations and OD pairs in an urban network. To this end, a method for data treatment of the 2017 dataset from the traffic monitoring system of Fortaleza, Brazil, was employed, which consisted in the following major steps: data cleaning due to equipment failure, definition of traffic profiles for typical and atypical months, definition of daily traffic periods, selection of suitable devices to obtain OD flows, and detection of outliers in the time series. The traffic profiles and the daily traffic periods were defined by applying clustering techniques. The analysis of autocorrelation was performed after controlling for seasonal effects in the data by applying regression analysis. This study contributes to understand how the dynamic of trip patterns varies over space due to the spatial distribution of the city's activities and the network's spatial centrality. The analysis of 144 sets of traffic volumes throughout 2017 suggests that the autocorrelation of traffic volumes should be higher in congested central areas where multiple options of route are available. It seems that, for large congested networks, which present many uncertain factors (e.g., accidents, variable weather, multiple paths, etc.), part of the users do not have complete knowledge of the network's performance, and must rely on experience and habit to decide their routes, especially at more centralized locations of the network. The analysis of serial correlation in the series of sample OD flows between regions showed that the city's central area, where more commercial and service-related activities take place, seems to influence the dynamic of OD flows, probably due to the occurrence of more non-commuting trips to the central area of the city.     

Spatial clustering of events on a network

In this paper a methodology is proposed to compute spatial concentrations of point-based events on a network. The distance along the network is used as a measure of the spatial closeness of events. The network is divided into statistical units, based on a random distribution of points of measurement and corresponding network segments, which are the statistical units of reference. For each segment a dangerousness index is computed which indicates the distance-weighted number of traffic accidents in the neighbourhood. The statistical significance of clusters of accidents is tested using a Monte Carlo simulation. The methodology is applied to traffic accidents to detect dangerous locations on the road network of the city of Brussels in Belgium.     

An optimal approach for a set covering version of the refueling-station location problem and its application to a diffusion model

This article deals with the refueling-station location problem for alternative fuel vehicles in a traffic network. Alternative fuel vehicles can be characterized by the vehicle range that limits the travelable distance with fuel at full capacity. I propose an efficient formulation of the refueling-station location problem using an optimal property and prove that the problem is NP(Non-deterministic Polynomial)-complete in the strong sense. I consider a special case of the refueling-station location problem in which the construction costs are equal for all nodes. In this case, the problem is to determine refueling station locations to minimize the total number of stations, while making the possible multiple predetermined origin–destination round-trips. I propose an optimal algorithm applicable when no refueling stations currently exist in a traffic network and a dynamic programming based algorithm applicable when a set of refueling stations already exists. I apply the algorithms to a traffic network to study the diffusion of refueling stations and predict the speed and range of station establishment. The computational experiments show that the speed of diffusion depends on the vehicle range and the sequence of the origin–destination demands considered in the diffusion process.     

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.     

日本铁路考察报告——对我国铁路产业及北京城市轨道交通发展方向的一些建议

轨道交通可以压缩时空距离,提升时空价值,实现交通功能与其他功能有机耦合,已成为解决人地关系紧张和＂城市病＂的重要甚至唯一途径。日本经验表明,轨道交通发展重点已从路网扩充向综合枢纽及铁路客站城市方向转移。快速轨道交通要适应城市群连绵成带发展需要,要大力发展通勤铁路和环状线,按照TOD模式实现交通枢纽与土地联合开发,构建铁路车站城市。我国要加强高速铁路和城市轨道交通时空规制,培育铁路文化、铁路精神和铁路意识,构建高速铁路建设触发机制,完善交通枢纽和综合运输规划体制,促进方式间有序竞争,形成财政补贴长效机制,实施计程票制和收益管理制度,优化运营组织,促进我国轨道交通事业可持续发展。     

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.     

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.     

Beyond expected regularity of aggregate urban mobility: A case study of ridesourcing service

The regularity of urban mobility is a tacit understanding in public transportation planning. Many studies investigated regularity in the context of individual travel while fewer were found on aggregate mobility. This work proposes a generic measure of regularity to quantify the degree of repetition of a time or spatial data series. In addition, it discusses how the properties of a data series itself may dictate the regularity and adopts parametric bootstrapping to estimate the residual regularity that deviates from realizations of stochastic processes. The proposed measure can examine not only the periodicity of trip generation in locations but also the stationarity of travel flows. The experiments were conducted on a ridesourcing dataset, including more than six million trips in Chengdu, China. Both the temporal and spatial regularities were investigated at distinct time intervals. We discussed how the total travel volume and the peakedness of probability distribution could affect the regularity. It was found that regularity was positively associated with the total volume and the peakedness. We further examined the impact of the built environment on regularity and the effects of spatial and temporal scales. The results show both point-of-interest density and diversity and tourist attractions contribute to regularity. Regularity grows linearly as the spatial and temporal scales increase exponentially. Lastly, different specifications of regularities were compared, and the outcome was generally consistent across different similarity measures including coefficient of determination, Manhattan distance, Euclidean distance, cross-entropy and cosine similarity.     

An AHP analysis of air traffic management with target windows

The main operational concept of Single European Sky ATM Research Programme is the notion of business trajectory. One possible implementation is based on the notion of a contract of objectives; an agreement among the main air traffic management actors on spatial and temporal intervals called target windows. These 4D windows are defined prior to flight departure by the airlines, airports and air navigation service providers to increase punctuality. We use an analytic hierarchy process to assess the opportunity of implementing this concept by considering the views of experts. The findings indicate that there are net benefits for airlines and air navigation service providers but not for airports     

Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city

Smart card data (SCD) allow analyzing mobility at a fine level of detail, despite the remaining challenges such as identifying trip purpose. The use of the SCD may improve the understanding of transit users' travel patterns from precarious settlements areas, where the residents have historically limited access to opportunities and are usually underrepresented in surveys. In this paper, we explore smart card data mining to analyze the temporal and spatial patterns of the urban transit movements from residents of precarious settlements areas in São Paulo, Brazil, and compare the similarities and differences in travel behavior with middle/high-income-class residents. One of our concerns is to identify low-paid employment travel patterns from the low-income-class residents, that are also underrepresented in transportation planning modeling due to the lack of data. We employ the k-means clustering algorithm for the analysis, and the DBSCAN algorithm is used to infer passengers' residence locations. The results reveal that most of the low-income residents of precarious settlements begin their first trip before, between 5 and 7 AM, while the better-off group begins from 7 to 9 AM. At least two clusters formed by commuters from precarious settlement areas suggest an association of these residents with low-paid employment, with their activities placed in medium / high-income residential areas. So, the empirical evidence revealed in this paper highlights smart card data potential to unfold low-paid employment spatial and temporal patterns.     

Location factors for logistics facilities: Location choice modeling considering activity categories

While many past studies analyze the changes in the spatial distribution of logistics facilities and provide insights on the factors that drive the migration of logistics facilities, only a few studies analyze the factors of location choice at the individual facility level. Furthermore, the differences among various facility types, with respect to the key locational characteristics that attract logistics facility developments, are still not clear. This research attempts to fill the research gap by analyzing the location choices for logistics facility development. Using the data from the Paris Region, we characterize the locations and activities of logistics facilities and estimate a logistics facility location choice model for each activity category. The analysis reveals the key locational characteristics that influence logistics facility locations, such as zoning regulations, wholesale job accessibility, population density, and the accessibility to autoroute (controlled-access highway), as well as the heterogeneity in the effects of these characteristics by activity category. Zoning and traditional clusters play a significant role for the locations of newly developed logistics facilities, which underlines the importance of public policies for logistics land use in the Paris region. The effect of the accessibility to population is identified for the group of facilities which serve for retail shops and end-consumers only under the assumption that alternative locations are limited to specialized economic activity/logistics zones, indicating that population accessibility is a secondary factor to the designation as the specialized zone. Zoning changes to/from economic activity/logistics zones may cause significant impacts on the spatial distribution of logistics facilities, and thus, the distribution of goods vehicle traffic flow.     

Spatial impact analysis of trans-Yangtze highway fixed links: A case study of the Yangtze River Delta,China

As an important infrastructure connecting straits or rivers, the construction of fixed links has become an effective measure for improving traffic conditions and promoting socio-economic development in many countries and regions around the world. Thus, it has become a significant topic in the field of transport geography. Taking the Yangtze River Delta as its case area, this study proposes a spatial impact model of trans-Yangtze highway fixed links, consisting of three components: an accessibility model, modified gravity model, and traffic utilisation model, which are used to analyse, respectively, cross-Yangtze accessibility and changes to transport structure, cross-Yangtze urban interaction and changes to economic structure, and the utilisation relationship of fixed links. Making use of existing fixed links while planning and building new ones has become the basis for achieving regional integration and sub-regional cooperation in this area, the aim of which is to create the barrier-free circulation of elements. The results prove that this spatial impact model coincides with reality. The increase in the number of fixed links has significantly shortened cross-river travel time and facilitated a unification of north-south highway networks. It has also promoted the formation of a high-connection urban network along the river. Northern urban nodes have joined the southern economic circle and finally achieved north-south integration. There are complex relationships between fixed links and their hinterlands, showing obvious utilisation gaps between the links. The complete system of trans-Yangtze fixed links is comprised of several river crossing facilities and connected highways. The function of each link is related to its role in the regional highway network. From a small number of fixed links to the realisation of multiple trans-Yangtze bridges and tunnels with easy access to multiple arterial highways, a ‘many-to-many’ spatial pattern is created that ultimately leads to the evolution of regional transport and economic structures.     

Mapping ridership using crowdsourced cycling data

Cycling volumes are necessary to understand what influences ridership and are essential for safety studies. Traditional methods of data collection are expensive, time consuming, and lack spatial and temporal detail. New sources have emerged as a result of crowdsourced data from fitness apps, allowing cyclists to track routes using GPS enabled cell phones. Our goal is to determine if crowdsourced data from fitness apps data can be used to quantify and map the spatial and temporal variation of ridership. Using data provided by Strava.com, we quantify how well crowdsourced fitness app data represent ridership through comparison with manual cycling counts in Victoria, British Columbia. Comparisons are made for hourly, AM and PM peak, and peak period totals that are separated by season. Using Geographic Information Systems (GIS) and a Generalized Linear Model we modelled the relationships between crowdsourced data from Strava and manual counts and predicted categories of ridership into low, medium, and high for all roadways in Victoria. Our results indicate a linear association (r² 0.40 to 0.58) between crowdsourced data volumes and manual counts, with one crowdsourced data cyclist representing 51 riders. Categorical cycling volumes were predicted and mapped using data on slope, traffic speeds, on street parking, time of year, and crowdsourced ridership with a predictive accuracy of 62%. Crowdsourced fitness data are a biased sample of ridership, however, in urban areas the high temporal and spatial resolution of data can predict categories of ridership and map spatial variation. Crowdsourced fitness apps offer a new source of data for transportation planning and can increase the spatial and temporal resolution of official count programs.     

An empirical assessment of Dutch citizens' preferences for spatial equality in the context of a national transport investment plan

This study presents empirical insights into Dutch citizens' preferences for spatial equality in the context of decision-making regarding the composition of a national transport investment plan. To the best of our knowledge, our study is the first study worldwide which empirically investigates citizens' preferences for the spatial distribution of benefits accruing from a transport investment plan. We conducted two Stated Choice experiments: one involving an investment plan for travel time savings, the other involving an investment plan for traffic safety. Our results show that in the context of travel time savings, a vast majority of citizens has a strong preference for spatial equality. When the investment program involves traffic safety improvements, the share of citizens that has a preference for spatial equality is considerably smaller. Specifically, using a Latent class discrete choice analysis we identified distinct segments. The first segment has a very strong preference for the investment program having the largest total reduction in traffic deaths; the second segment assigns a substantial value to an equal distribution of reductions of traffic deaths across the Netherlands. Highly educated citizens are found to have a relatively strong preference for spatial equality as compared to low educated citizens. Contrary to our expectations, explanatory variables such as political orientation, income, car ownership and region of residence do not appear to associate with citizens' preferences for spatial equality.     

Monitoring trucks to reveal Belgian geographical structures and dynamics: From GPS traces to spatial interactions

Despite the fact that freight transport has a huge impact on the economy and the environment, Belgian datasets have always been scarce or restricted to very small a-spatial samples. Spatial data collected in Belgium for toll-paying trucks are here examined, and geographical structures and dynamics are extracted from this massive dataset. The originality of this dataset is its exhaustivity and its real-time approach: the location of all the trucks circulating in Belgium is collected every 30 s.The paper first relates to the methodology applied when using and transforming big data generated by On Board Units GNSS (cleaning, transforming and pre-processing). Second, it maps and comments on the movements (traffic) and stops of trucks within the whole country, providing a clear picture of the Belgian situation, useful for regional planners and logistics companies. Finally, the flows of trucks observed between Belgian locations enable the country to be divided into mathematical communities of places that interact the most. Analyses are performed for sub-categories based on the country of registration, underlining the spatial specificities of freight transit in Belgium. This exploratory spatial data analysis enables to reveal not only multi-level spatial structures associated with urban hierarchies and the transport infrastructure, but also firm locations or political organizations and to consider the complexity and interconnectivity of any measure taken for a more sustainable future. With a clear methodological framework to cope with the data pre-processing, this paper opens the way to various potential applications linked with freight transportation in Belgium.     

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.     

Delineating traffic congestion zones in cities: An effective approach based on GIS

Identifying congested areas for implementing road pricing is a complicated task, especially as traffic speed and other census data may not be readily available before a detailed feasibility study is commissioned. In this study, we introduce a spatial approach to identify areas with road traffic congestion within cities. The results obtained are validated against the empirical traffic speed data in four Asian cities, i.e. Bangkok, Hong Kong, Kuala Lumpur, Singapore, from 2018 to 2020. Moreover, the findings are also compared with a more detailed conventional method of using job density to identify areas of high traffic congestion. The comparisons show that the spatial approach is both efficient and effective. Through applying the method to London, Milan and Stockholm, the study also demonstrates other potential empirical applications in reviewing the boundaries of existing congestion charging zones.     

Spatial patterns and temporal dynamics of urban bicycle crashes—A case study from Salzburg (Austria)

Most bicycle crash analyses are designed as explanatory studies. They aim to identify contributing risk factors and calculate risk rates based on – most of the time – highly aggregated statistical data. In contrast to such explanatory study designs, the presented study follows an exploratory approach, focusing on the absolute number of crashes. The aim is to reveal and describe patterns and dynamics of urban bicycle crashes on various spatial scale levels and temporal resolutions through a multi-stage workflow. Spatial units are delineated in the network space and serve as initial units of aggregation. In order to facilitate comparisons among regions and quantify temporal dynamics, a reference value of crash frequency is simulated for each unit of the respective spatial scale level and temporal resolution.For the presented case study, over 3000 geo-coded bicycle crashes in the city of Salzburg (Austria) were analyzed. The data set covers 10 years and comprises all bicycle crashes reported by the police. Distinct spatial and temporal patterns with clusters, seasonal variations, and regional particularities could be revealed. These insights are indicators for urban dynamics in the transport system and allow for further, targeted in-depth analyses and subsequent counter measures. Moreover, the results prove the applicability of the proposed multi-stage workflow and demonstrate the added value of analyses of small aggregates on various scale levels, down to single crashes, and temporal resolutions.