Understanding road network dynamics: Link-based topological patterns

Road network interruptions caused by natural disasters are becoming more frequent and their consequences are becoming of a wider range. The main goal of this work is to identify the most important roads in a network. Herein, a new model is proposed to evaluate the most important roads in the network through the application of biclustering technique, identifying patterns of attributes (road performance measures) and patterns of roads (connectivity patterns). Thereafter the model presented here is compared with the mean geodesic distance variation. Both methodologies are applied to a case study and the pros and cons are discussed as well. Results point out the alpha index as the topological measure more relevant in the normal network flow; moreover the interruption of the links with highest values of connectivity will have larger consequences in the normal functioning of the network than the links with the lowest levels of connectivity. The approach here proposed is a useful insight of the network dynamics, which allows optimizing the worst-case performance of the system. This work can be useful for risk management actors, for civil protection agents, who need to decide on the effective allocation of human and physical resources and define priority areas, and for the government institutions which design the network of facilities.     

Optimizing road network daily maintenance operations with stochastic service and travel times

This paper studies optimization methods for a routing problem encountered in daily maintenance operations of a road network. Stochastic service and travel times on road segments are considered. The problem is formulated as a variation of the capacitated arc routing problem (CARP). A chance-constrained programming model is firstly developed and solved by a branch-and-cut algorithm. A stochastic programming model with recourse is also proposed to take into account the recourse costs in case of route failure. The problem is solved by an adaptive large neighborhood search algorithm. The computational experiments demonstrate the effectiveness of the algorithm.     

Revealing travel patterns and city structure with taxi trip data

Delineating travel patterns and city structure has long been a core research topic in transport geography. Different from the physical structure, the city structure beneath the complex travel-flow system shows the inherent connection patterns within the city. On the basis of taxi-trip data from Shanghai, we built spatially embedded networks to model intra-city spatial interactions and to introduce network science methods into the analysis. The community detection method is applied to reveal sub-regional structures, and several network measures are used to examine the properties of sub-regions. Considering the differences between long- and short-distance trips, we reveal a two-level hierarchical polycentric city structure in Shanghai. Further explorations of sub-network structures demonstrate that urban sub-regions have broader internal spatial interactions, while suburban centers are more influential on local traffic. By incorporating the land use of centers from a travel-pattern perspective, we investigate sub-region formation and the interaction patterns of center–local places. This study provides insights into using emerging data sources to reveal travel patterns and city structures, which could potentially aid in developing and applying urban transportation policies. The sub-regional structures revealed in this study are more easily interpreted for transportation-related issues than for other structures, such as administrative divisions.     

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.     

Socio-occupational and geographical determinants of the frequency of long-distance business travel in France

Business travel, and long-distance business travel in particular, has received much less attention in research than home-to-work travel. This article explores some determinants of the frequency of long-distance business travel. It focuses on the influence of two parameters that have been little studied: the individual’s socio-occupational group and the workplace location. The data used were provided by the 2008 French National Transport and Travel Survey. Following a review of the literature, a Poisson regression is performed on the frequency of long-distance business trips. The most significant determinants are socio-occupational category, income, gender and the urban or rural nature and size in terms of the number of inhabitants of the area of location. In particular long-distance business trip frequency is lower in the Paris urban area than in all the categories of urban areas as well as rural areas because of the highly centralised organisation of economic activity and the transport network in France.     

Examining and identifying the determinants of travel expenditure patterns

This study examined the effects of socio‐demographic, travel‐related and psychographic variables on travel expenditures. The travel expenditure categories examined include lodging, meals and restaurants, attractions and festivals, entertainment, shopping, transportation and total expenditures. The results of the study provide a more comprehensive and holistic picture in the search of travel expenditure patterns based on multiple independent variables. This study reveals that, among the three groups of variables examined, income and trip‐related characteristics were the most influential variables affecting tourism expenditures. Discussions and implications are also provided based on the study results. Copyright © 2006 John Wiley & Sons, Ltd.     

Spatial context and the complexity of daily travel patterns: an international comparison

The analysis of travel patterns is an important research topic in transportation research and urban planning. It provides the background information necessary to better understand the complex relationship between urban structure, the transportation system and household travel patterns. To what extent do travel behaviour reflect the properties of the urban structure and the transportation network, or do these patterns largely follow their own regularities? Can different patterns be observed across different space-time settings, or can common patterns be observed, largely independent from such contexts? To better understand these relationships, this paper reports on some of the findings of analyses, conducted to identify underlying structures in various aspects of travel patterns. Travel patterns, derived from activity and travel diary data collected in Portland (USA), Midlands (UK), Fukuoka (Japan), Canadian metropolitan areas, and the South-Rotterdam region (The Netherlands) are compared. The results indicate that travel patterns are largely independent from spatial setting, except for some extreme cases.     

Decentralizing Pareto-efficient network flow/speed patterns with hybrid schemes of speed limit and road pricing

This paper examines the potential of hybrid schemes of speed limit and road pricing for decentralizing Pareto-efficient flow/speed patterns that minimize total travel time and total emissions simultaneously. Both link flows and speeds are treated as independent variables in our bi-objective formulation. The resulting Pareto frontier is thus weakly dominant to that in previous literature. For any such favorable Pareto-efficient flow and speed pattern, we establish the existence of hybrid schemes of speed limits and non-negative/revenue-neutral tolls, whose set of user equilibrium (UE) solutions contains the Pareto-efficient one, and provide sufficient conditions under which the Pareto-efficient solution can be certainly obtained.     

Hub competition and travel times in the world-wide airport network

The aim of this work is to measure the competition between airport hubs based on an analysis of travel times in the world-wide airport network. By considering the minimum travel time required to connect each pair of airports, it is possible to create new measures of hub competition, separating the effects of hub position and temporal coordination. This analysis was carried out at the global level, considering all 232 airports with more than 3 million seats yearly offered in departure flights in 2008, and also in relevant geographic markets. The results show a high level of competition among the most important world airports, but the major airports of Europe have a geographical advantage in relation to world markets over the major American and Asian airports. We also show that airports located in different continents often compete for the same origin–destination markets. Geographical position appears to be the most important variable explaining hub performance. Secondary hubs show a higher degree of specialization towards specific markets.     

Lessons from travel planning and road user charging for policy-making: through imperfection to implementation

In 1978 Gunn published a seminal paper which explained why implementation of policy is so difficult. The paper set out 10 conditions, which should be satisfied if perfect implementation is to be achieved. Whilst it is clear that perfect implementation is not possible in the real world, and Gunn has subsequently been criticised for his ‘top-down’ approach to decision-making, these conditions do, nonetheless act as an effective framework through which to evaluate good practice in the implementation of urban transport policy instruments. Two urban transport policy instruments, which form an increasingly important element of the Government's strategy in the UK for reducing the demand for private transport as set out in a New Deal for Transport (DETR, 1998), are travel plans and road user charging. Travel plans are a relatively recent policy instrument in the UK and seek to reduce trips to work by car by providing, through individual employers, a targeted, integrated package of incentives and disincentives to influence commuters' choice of mode of travel to and from the workplace. Road user charging, whereby motorists are charged for the road space they use in urban areas, seeks to reduce the congestion problem via the price mechanism, and has a longer history in the UK. To date the implementation of travel plans in the UK has been more widespread than that of road user charging. It is fair to say, however, that the widespread implementation of both urban transport policy instruments is a complex and sensitive area for decision-makers.The aim of this paper is firstly, to analyse travel plans and road user charging in the UK with respect to the conditions for perfect implementation put forward by Gunn and secondly, to highlight the elements of good practice, pertinent to the implementation of road user charging, in the process of the implementation of travel plans. Overall, the paper uses Gunn's theoretical framework as a basis for recommendations for better decision-making that will aid the wider implementation of both travel plans and road user charging internationally.     

Understanding the travel behaviors and activity patterns of the vulnerable population using smart card data: An activity space-based approach

Understanding the travel behaviors and activity patterns of vulnerable people is important for addressing social equity in urban and transportation planning. With the increasing availability of large-scale individual tracking data, new opportunities have emerged for studying people's travel behaviors and activity patterns. However, the data has not been fully exploited to examine the travel characteristics of vulnerable people and their implications for understanding transport-related disadvantage. This study proposes a methodological framework based on the concept of activity space that enables a comprehensive examination of vulnerable people's spatiotemporal travel characteristics and an investigation of the geographies of transport disadvantage. Using the proposed framework, a case study that investigates the bus activities of the vulnerable population using four-month smart card data is carried out in the city of Wuhu, China. The case study suggests that vulnerable people possess distinct travel behaviors that differ considerably from the mainstream population and that the implications of transport disadvantage, as revealed by the participation in bus activities, vary across different demographic groups and across different spatial contexts. Some of the empirical insights obtained from this study also differ from conclusions drawn from previous studies and will enrich our understandings of vulnerable people's activities. Overall, the paper makes two major contributions. Methodologically, the proposed framework can overcome some of the deficiencies of activity space-based approaches for understanding transport disadvantage and contribute broadly to the studies of travel behaviors and activities patterns using individual-level tracking data. Empirically, the study identifies varying spatial and temporal implications of transport disadvantage associated with different vulnerable groups, which could further shed light on public transit planning and service design.     

Transportation network and venture capital mobility: An analysis of air travel and high-speed rail in China

Transportation network plays a critical role in reshaping the spatial geographical economy in terms of population, urban form, output and so on. But the impact of transportation on capital mobility is seldom revealed. Using venture capital investment events and airline network as well as high-speed rail network in China, this paper examines the effects of transportation network on capital mobility. We find that 1% decline in travel time will lead to increase in VC investment deals by 0.02. Heterogeneous results indicate that small firms, young firms, and emerging industries benefit more from the transportation network, which demonstrates the role of transportation network in alleviating information asymmetries. In addition, heterogeneity on VC flow direction offers suggestive evidence that transportation network is likely to alleviate development imbalance within wealthier cities while widening the gap between wealthier cities and poorer cities.     

Exploring the network structure and nodal centrality of China’s air transport network: A complex network approach

This paper uses a complex network approach to examine the network structure and nodal centrality of individual cities in the air transport network of China (ATNC). Measures for overall network structure include degree distribution, average path length and clustering coefficient. Centrality metrics for individual cities are degree, closeness and betweenness, representing a node’s location advantage as being directly connected to others, being accessible to others, and being the intermediary between others, respectively. Results indicate that the ATNC has a cumulative degree distribution captured by an exponential function, and displays some small-world (SW) network properties with an average path length of 2.23 and a clustering coefficient of 0.69. All three centrality indices are highly correlated with socio-economic indicators of cities such as air passenger volume, population, and gross regional domestic product (GRDP). This confirms that centrality captures a crucial aspect of location advantage in the ATNC and has important implications in shaping the spatial pattern of economic activities. Most small and low-degree airports are directly connected to the largest cities with the best centrality and bypass their regional centers, and therefore sub-networks in the ATNC are less developed except for Kunming in the southwest and Urumchi in the northwest because of their strategic locations for geographic and political reasons. The ANTC is relatively young, and not as efficient and well-developed as that of the US.     

Measuring segregation using patterns of daily travel behavior: A social interaction based model of exposure

Recent advances in transportation geography demonstrate the ability to compute a metropolitan scale metric of social interaction opportunities based on the time-geographic concept of joint accessibility. The method we put forward in this article decomposes the social interaction potential (SIP) metric into interactions within and between social groups, such as people of different race, income level, and occupation. This provides a novel metric of exposure, one of the fundamental spatial dimensions of segregation. In particular, the SIP metric is disaggregated into measures of inter-group and intra-group exposure. While activity spaces have been used to measure exposure in the geographic literature, these approaches do not adequately represent the dynamic nature of the target populations. We make the next step by representing both the source and target population groups by space–time prisms, thus more accurately representing spatial and temporal dynamics and constraints. Additionally, decomposition of the SIP metric means that each of the group-wise components of the SIP metric can be represented at zones of residence, workplace, and specific origin–destination pairs. Consequently, the spatial variation in segregation can be explored and hotspots of segregation and integration potential can be identified. The proposed approach is demonstrated for synthetic cities with different population distributions and daily commute flow characteristics, as well as for a case study of the Detroit–Warren–Livonia MSA.     

Shopping orientation and online travel shopping: The role of travel experience

This paper investigates how consumers' shopping orientation toward travel shopping influences their tendency to shop for travel products on the Internet. The paper also looks into the role of consumers' travel experience. A conceptual model is developed and a number of hypotheses are forwarded and tested by structural equation modeling, using data from 256 respondents. The results support several of the stated hypotheses. Shopping orientation and travel experience both influence travelers' intentions to shop travel products online. Implications for management practice and further research are discussed at the end of the paper. Copyright © 2011 John Wiley & Sons, Ltd.     

Airline network structure and the gravity model

In this paper, we determine characteristics of the cost-minimizing airline network under economies of density. Airline demand is asymmetric and governed by the gravity model. Airline networks are restricted to those where each spoke city is assigned to a single hub and where hub cities are fully interconnected. The cost-minimizing network is a mixture of a point-to-point and a single hub network. Multi-hub networks where passengers change planes at more than one airport are found to be suboptimal.     

Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel

Traffic-related carbon dioxide (CO₂) emissions have become a major problem in cities. Especially, the CO₂ emissions induced by taxis account for a high proportion in total CO₂ emissions. The availability of taxi trajectory data presents new opportunities for addressing CO₂ emissions induced by taxis. Few previous studies have analyzed the impact of human trips on CO₂ emissions. This paper investigates trip-related CO₂ emission patterns based on individuals' travel behavior using taxi trajectory data. First, we propose a trip purpose inference method that takes into account the spatiotemporal attractiveness of POIs to divide human trips into different types. Further, we reveal the spatiotemporal patterns of CO₂ emissions from various types of trips, including temporal regularity and periodicity as well as spatial distribution of “black areas”. Finally, comparative analysis of CO₂ emissions for different kinds of trips based on trip behavior is conducted using three variables, namely trip distance, trip duration and trip speed. This study is helpful for us to understand how to make travel and cities more sustainable through modifying people's trip behaviors or taxi trips.     

Examining commuting patterns using Floating Car Data and circular statistics: Exploring the use of new methods and visualizations to study travel times

In this paper, we focus on the spatial distribution of travel time differences between car and public transport during peak and off-peak hours using geospatial methods and circular statistics. To that end, we combine two origin-destination matrices: one contains the number of simulated commuting trips and the other contains accurate travel times measured using either Floating Car Data or transit data. The combination of both matrices reveals that differences in travel time during peak and off-peak hours are high for intra-city commuting and low for inter-city commuting.