Features Of Transportation Network Research Articles

The effect of the built environment on spatial-temporal pattern of traffic congestion in a satellite city in emerging economies

Traffic congestion has been widely perceived as an inevitable byproduct in the process of global urbanization, leading to significant negative economic and environmental impacts. Existing studies have attached significant importance to revealing the interaction between traffic congestion patterns and built environment features in many metropolises with little attention, if any, paid to their counterparts of satellite cities (SC). For being aware that developing SCs has been a main trend to address many urban issues of metropolises in the rapid urbanization process of emerging economies, this study makes one of the first research attempts to investigate how traffic congestion temporally and spatially evolves with the built environment characterized by land use and transportation network features in Foshan, a typical SC of Guangzhou in China by using valuable hyperlocal travel data. The results show that while weekdays experience traffic peaks in both morning and evening, weekends or holidays generally only have the evening peak. The traffic congestion in Foshan during the pre-weekend is found much heavier compared with normal weekdays and weekends with a long-lasting effect possibly due to Foshan's role as a SC of Guangzhou. Additionally, the rising traffic congestion in a SC associated with company land use during the peak hours can be partly offset by the increasing supply of public transit, suggesting urban planners increase the density of public transit where there is a denser distribution of companies. This study provides new knowledge on travel behaviors in SCs in emerging economies, supplying urban governors with new insights for improving their traffic conditions generally.

Finding evacuation routes using traffic and network structure information

• Evacuation framework using topology and space-time traffic information is proposed. • The proposed algorithm greatly improves network clearance time during peak times. • Proposed routes ensure seamless evacuations but evacuees must cover long distances. • Properly maintaining central links and boosting connectivity in networks is vital. Given the unpredictable nature of transportation networks, a strategic disaster evacuation plan involving taking detours, which avoids congested and less connected links, is of practical importance. It guarantees the seamless evacuation of evacuees to safe exits while reducing the chance that their safety is jeopardized. This study develops a vehicle evacuation algorithm that employs a link-based centrality metric to dynamically identify agile evacuation paths based on the topological features and the changing spatio-temporal traffic concentration on links within the network. By employing real traffic volume data corresponding to the Yeouido Island’s transport network in Seoul, Korea, we demonstrate that, relative to the conventional shortest path approach, considering the structural and operational features of transport networks using a link-based betweenness centrality measure has a better evacuation performance during high evacuation demand periods by lowering network clearance time. Extensive experimental results, pertinent findings, and recommendations for developing effective evacuation plans are discussed.

City-level urban form and traffic safety: A structural equation modeling analysis of direct and indirect effects

While most existing studies have examined the impact of the community- and street-level built environment on traffic safety, few have provided empirical evidence on the associations between urban form characteristics and traffic safety at the city level. To this end, this article first created a detailed list of 23 variables to measure city-level urban form of 100 major Urban Areas (UAs) in the United States and then applied factor analysis to construct five latent variables which describe urban form. Factor analysis is also used to define mediator variables reflecting citywide transportation network features and dependent variable of traffic safety. Structural Equation Modeling (SEM) is then used to investigate how city-level urban form, directly and indirectly (through mediators of transportation network features), affects traffic safety. Based on the statistical results, urban traffic is safer in UAs with more uniform job-housing balance among their different tracts, more polycentric design, and less low-density sprawl. In addition to spatial variation in employment and urban density that have significant direct effect on traffic safety, improving transportation network connectivity and increasing the supply of public transit facilities and upper-level transport infrastructures can decrease traffic fatalities indirectly, through encouraging the use of non-driving transport modes. It is estimated that a 10% increase in urban density as well as a 10% increase in even spatial distribution of employment can reduce the rate of fatal crashes by >15%, on average. These findings demonstrate the importance of incorporating city-level land-use policies into the planning practices, in terms of traffic safety.

Travel analytics: Understanding how destination choice and business clusters are connected based on social media data

Understanding how destination choice and business clusters are connected is of great importance for designing sustainable cities, fostering flourishing business clusters, and building livable communities. As sharing locations and activities on social media platforms becomes increasingly popular, such data can reveal destination choice and activity space which can shed light on human-environment relationships. To this end, this research models the relationship between characteristics of business clusters and check-in activities from Los Angeles County, California. Business clusters are analyzed via two lenses: the supply side (employment data by industry) and the demand side (on-line check-in data). Spatial and statistical analyses are performed to understand how land use and transportation network features affect the popularity of the identified clusters and their relationships. Our results suggest that a cluster with more employment opportunities and more types of employment is associated with more check-ins. A business cluster that has access to parks or recreational services is also more popular. A business cluster with a longer road network and better connectivity of roads is associated with more check-ins. The visualization of the common visitors between clusters reveals that there are a few clusters with outstanding strong ties, while most have modest ties with each other. Our findings have implications on the influence of urban design on the popularity of business clusters.

Container Ports Multimodal Transport in China from the View of Low Carbon

The study uses the carbon dioxide emission calculation model published by IPCC to measure carbon dioxide emissions and fuel inputs of the three types of multimodal transport (road-sea, railway-sea, and river-sea) in ports of China. Then, we make a case study on Shanghai port. Combined with carbon taxes launched around 2012 in China, this paper calculates the carbon taxes on the three types of multimodal transport and makes a pairwise comparison between roadway/railway, roadway/waterway and waterway/railway. The results show that increasing the proportion of railway-sea transportation and river-sea transportation to a reasonable level will achieve great energy saving, emission reduction, and economic benefits. According to different transportation network features, this paper applies Cluster analysis to raise separately suggestions for long-term development of coastal container ports in China based on low-carbon thinking.

Automatic Processes in Database Building and Subsequent Automatic Abstractions

One of the difficulties in building an effective system for spatial and attribute generalization is the predominance of the conventionally based generalization paradigm. This is exemplified by numerous attempts at reproducing rules derived from manual environments and re-building them in a GIS or CIS. What is often lacking is an understanding of what generalization achieves in its various forms. There is no doubt that the representation issue in generalization is of critical importance, since it is through the representation that we communicate visually. However, with access to databases and high-level programming tools, the quality, consistency, and reliability of the information warrants far greater emphasis than has been given in the past. Re-addressing generalization from a database perspective shifts the focus in model development from cartographic representation issues to geographic information. However, doing so places demands on operational mapping organizations for database building, data classification, and subsequent feature coding. Recently, techniques have been developed to automatically classify, code, and build databases. Since the impediment to generalization has rested partially in establishing a logical database, the approach discussed in this chapter is to identify techniques that allow users to create the database automatically, without having to code the features interactively. Three different data sets being used for experimentation include hydrography, transportation networks, and land-cover. The models differ somewhat, given the nature of the object categories. Hydrographic and transportation-network features can be classified and coded using a rule base and graph theory, while land cover can be handled using classification and aggregation hierarchies. The objective of this research is to load unclassified categories of features, derive a series of classifications automatically, build a database automatically, and subsequently generalize the data using the derived database to any smaller scale of representation. The objective of this chapter is to present the methodologies required to achieve the research goals and to illustrate the results.