A model of the rise and fall of roads

Zhang, Lei; Levinson, David

doi:10.5198/jtlu.2016.887

Cited by 8 publications

(9 citation statements)

References 39 publications

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“…Tian et al (2015) assessed relationship between traffic generation and mixed-use development. Briefly, main built environmental factors affecting traffic state or travel behavior could be divided into trafficrelated (Hahn et al, 2002;Feng, Li, Zhao, & Hu, 2011;Zhang & Levinson, 2017) and land-use related (Wheaton, 1998;Handy, Cao, & Mokhtarian, 2005) ones. Based on the previous researches, variables chosen for further analysis are purposed as follows: 1) Traffic-related factors: F1: Road type, primary or secondary road.…”

Section: Spatial Analysis Of Road Segmentsmentioning

confidence: 99%

Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data

Zhang

Sun

Shen

et al. 2017

JTLU

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Abstract:With the development of in-vehicle data collection devices, GPS trajectory has become a priority source to identify traffic congestion and understand the operational states of road network in recent years. This study aims to investigate the relationship between traffic congestion and built environment, including traffic-related factors and land use. Fuzzy C-means clustering was used to conduct an exhaustive study on the 24-hour congestion pattern of road segments in an urban area, so that the spatial autoregressive moving average model (SARMA) could be introduced to analyze the output from the clustering analysis to establish the relationship between built environment and the 24-hour congestion pattern. The clustering result classified the road segments into four congestion levels, while the regression explained the impact of 12 traffic-related factors and land-use factors on the road congestion pattern. The continuous congestion was found to mainly occur in the city center, and the factors, such as road type, bus station in the vicinity, ramp nearby, commercial land use, and so on, had large impacts on congestion formation. The Fuzzy C-means clustering is proposed to be combined with quantitative spatial regression, and the overall evaluation process will assist to assess the spatial-temporal levels of service regarding traffic from the congestion perspective.Keywords: Congestion pattern, taxi GPS data, fuzzy C-means clustering, spatiotemporal regression, built environment factor IntroductionIn urban road network, the recurrent or current congestion of a certain road segment may largely impact the local network and reduce travel efficiency. Consequently, it is important to identify the Compared with mobile phone data, floating car data, cargo transport vehicle record and navigation system, taxi GPS trace data is one of the easiest available sources for accurate travel route and travel time records for a wider area with more road details. Data mining based on taxi trip can be traced back to the 1970s (Goddard, 1970), which has been applied to a wide range of studies, mainly including activity-based and infrastructure-based fields. The activity-based studies mostly focus on driver behavior, supply-demand pattern, and traffic state analysis, while the infrastructure-based studies mainly focused on lanes channelization (Tang, Yang, Kan, & Li, 2015) and signal-timing estimation (Yu & Lu, 2016).From driver behavior perspective, Zhang, Qiu, Duan, Du, and Lu (2015) proposed a space-time visualization method to demonstrate taxi daily trajectories by GIS-T to recognize working time, operating range, and residence location without time division. Qing, Parfenov, and Kim (2015) compared direct extracted datas like travel distance, speed, demand, and supply mismatch of taxi trip between fair weather and extreme storm using Manhattan GPS data, and discovered the reduction in trip distance and supply of drives during the extreme storm. Meanwhile, Hwang, Wu and Jian (2006) used structural equation modeling technique...

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Section: Spatial Analysis Of Road Segmentsmentioning

confidence: 99%

Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data

Zhang

Sun

Shen

et al. 2017

JTLU

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“…In the 1960s, several studies examined possible forces shaping transportation networks (Boyce 1963, Garrison andMarble 1965). Only recently has their been a revived small but growing interest in studying the growth of transportation networks (Levinson and Yerra 2002, Levinson and Karamalaputi 2003, Yamins et al 2003, Zhang and Levinson 2003, Verhoef and Rouwendal 2004). An important intellectual merit of studies of the network growth problem is that they could improve and extend the understanding of how transportation networks grow and decline, and help theorize the intertwined process of the growth in travel demand and the growth of the transportation network.…”

Section: Discussionmentioning

confidence: 99%

“…The expansion and contraction of a network could be modeled as the outcome of an evolutionary game played by those agents. The complexity in a transportation network also suggests an agent-based simulation approach, which has been pursued in several exploratory studies Levinson 2003, Zhang andLevinson 2004b). Future research may apply these methods to study the network growth problem and its planning implications.…”

Section: Discussionmentioning

confidence: 99%

The Economics of Transportation Network Growth

Zhang

Levinson

Essays on Transport Economics

Self Cite

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A number of factors influence the efficiency, productivity, and welfare of transportation networks. Travel demand, user costs, and facility supply costs equilibrate on various time scales under a set of pricing (taxes and tolls), investment and ownership policies. Two types of equilibria exist in a transportation network, short-run traffic equilibrium and long-run supply-demand equilibrium. The phenomenon of traffic equilibrium is explored with a fixed transportation network where the capacity of links is given. Even though investment-and ownershiprelated policies are not of major concern for studies of traffic equilibrium, it is still a complex problem due to network congestion effects, variations of pricing rules, and multidimensionality of user choices. In order to understand the long-run supply-demand equilibrium in a transportation network, one has to consider all of the above-mentioned factors in a coherent analytical framework. We refer to this research problem as the transportation network growth problem, because the network evolves and link capacity is not fixed in the long run.The growth (and decline) of transportation networks obviously affects the social and economic activities that a region can support, yet the dynamics of how such growth occurs is one of the least understood areas in transportation, geography, urban economics, and regional science. The growth of the transportation network is determined by the total amount of investment and the investment rule, both of which could change over time. What has become known as the network design problem in the transportation literature simplifies the network growth problem in three aspects: (1) investment decisions are considered independent of pricing rules and ownership structures; (2) only the optimal investment rule is considered; (3) inter-dependencies of sequential investment decisions are ignored. In

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“…The models for traffic assignment to transportation networks simulate how demand and supply interact and play a central role in developing a complete model. The results of such models describe a possible state of the system itself, or the 'average' state and its variation if available, and their results, in terms of traffic flows, are the inputs for the design and/or evaluation of projects (CONDUITS, 2011;Levinson and Karamalaputi, 2003;Zhang and Levinson, 2004). For our purpose we retain the principle represented by equation ( 21) but replace it by a simpler procedure: the facility type is upgraded each time the link volume over capacity (VOC) ratio (also known as saturation rate) reaches the value of 0.85, which we assume to be the start of congested behaviour.…”

Section: An Application In Transport Planningmentioning

confidence: 99%

“…Investment decisions depend on the level of service provided by specific links along with the effective costs evaluated through parametric indicators. Moreover, vehicle tolls can be considered for those roads which have annual revenue from traffic flows (de Palma et al, 2012;Zhang and Levinson, 2004). In this case study no toll is considered for the arterial type of link (though it is possible to consider it inside the local taxes amount) while investment for a multilane highway is only reported for private investor analysis.…”

Section: An Application In Transport Planningmentioning

confidence: 99%

The Evolution and Planning of Hierarchical Transport Networks

Martinis

Pagliara

Wilson

2014

Environ Plann B Plann Des

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The continuing growth of urban regions coupled with their strong interactions with transportation systems places great emphasis on the coevolution of these systems. In this paper we introduce three developments. First, as a preliminary, the dynamic model of the evolution of retail structure is extended to show how hierarchies emerge. Secondly, these ideas are applied to a model of the evolution in transport networks. Thirdly, it is shown that such a model can be translated into a planning context and the notion of an explicitly hierarchical dynamic model is introduced. An example of the use of such a model of evolution within a network planning context is presented. This explores the planning of network evolution in a case study of Naples arising from a new harbour which is under construction. The model provides a new method for transport system planning through combining the insights from dynamic modelling, particularly with respect to the sequencing of investment, with the practicalities of budget constraints.

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A model of the rise and fall of roads

Cited by 8 publications

References 39 publications

Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data

Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data

The Economics of Transportation Network Growth

The Evolution and Planning of Hierarchical Transport Networks

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