Spatial interpolation of traffic counts based on origin–destination centrality

This paper presents a new method to estimate Annual Average Daily Traffic. Often traffic volumes are estimated based on roadway characteristics, such as number of lanes, speed limit, and adjacent land use. However, for many communities, especially small communities, these attributes are uniform across roadway types and therefore unable to adequately explain observed variation in traffic volumes. The new method uses novel explanatory variables that are intrinsically derived through a modified form of centrality, a network analysis metric that quantifies the topological importance of a link in a network. The new approach requires minimal data collection and is easily executed using a geographic information system. The case study showed high quality results (out-of-sample validation R² = 0.95). The new approach can be used for various activities related to transportation planning and investment decision making.     

Exploring environmentally sustainable traffic signal warrant for planning application

With the concerns over the greenhouse gas (GHG) emissions associated with the surface transportation, the environmentally sustainable traffic signal warrant is essential to provide guidelines for making environmentally conscious decisions about control types at intersections. This research conducted a pilot study on developing CO₂ emissions-oriented traffic signal warrants. Intersection control types that were analyzed for this research include two-way stop, four-way stop, traffic signal, and roundabout. Two environmentally sustainable traffic signal warrants were proposed: Type I (without roundabout) and Type II (with roundabout). The proposed traffic signal warrants were compared with the existing mobility-oriented traffic signal warrant of the Manual on Uniform Traffic Control Devices (MUTCD) in terms of CO₂ emissions and total delay. The measurements were collected and estimated using a microscopic traffic simulation tool, Verkehr In Stadten—SIMulationsmodell (VISSIM). The environmentally sustainable traffic signal warrants show that the annual CO₂ emissions of the ground transportations in the USA were reduced by 7.2% with Type I warrant and 13.9% with Type II warrant.     

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.     

Implementing smartphone enabled collaborative travel: Routes to success in the tourism domain

Smartphone technology can help identify current and anticipate future patterns of behaviour and, with its social networking capabilities, allow users to imagine and organise collaborative travel opportunities, such as lift share. This has led to the development of collaborative apps designed to enable activities like lift sharing. Such apps require new norms of behaviour to establish a user base and research has yet to address the socio-cultural barriers to both the use of this technology to organise travel and the sharing of personal space that collaborative travel entails. This paper reports the findings of a study which designed, built and tested a collaborative travel app in the tourism domain. Data derived from exploratory interviews, post-trial interviews and a questionnaire reveal that user age and extent of mobile engagement play a less significant role than expected, while other aspects of the social exchange, notably social tie strength, trust and obligations play a more marked role. A conceptual framework and discussion of strategies to address these barriers provides insight into appropriate contexts and routes for implementation of collaborative travel apps.     

Goodbye maps,hello apps? Exploring the influential determinants of travel app adoption

With the increased market penetration of smartphones and the emergence of high-speed wireless network technologies, the tourism industry's interest in using mobile applications as a means to enhance the tourist experience is rising. However, despite the growing number of companies and organizations that are investing in travel-related apps, there is limited academic research on consumers' intentions to adopt these technological innovations. In this study, the technology adoption model, innovation diffusion theory, and social cognitive theory are utilized to investigate the factors that could affect travel app adoption by tourists visiting rural tourism sites in China. The results show that perceived usefulness, perceived ease of use, and compatibility are significant antecedents of the intention to use travel apps. Further, self-efficacy indirectly influences the intention to use travel apps through the mediation of outcome expectations. The results do not support social norms as significant predictors of behavioural intention. A discussion and implications are also provided.     

Pricing strategies for a taxi-hailing platform

Taxi hailing apps that facilitate taxi-customer matching quickly become popular in recent years. By combining the theories of two-sided market and taxi market, this paper models the taxi market in the presence of a single taxi hailing app through an aggregate and static approach. Based on the equilibrium model, the existence and stability of equilibria are examined, and a partial-derivative-based sensitivity analysis is conducted to quantitatively evaluate the impacts of the platform’s pricing strategies to the taxi market performance. The features of desirable price perturbations that improve social welfare and/or the platform’s profitability are also characterized.     

What is the busiest time at an airport? Clustering U.S. hub airports based on passenger movements

Accurate clustering of airports enables airport authorities and operators to precisely position themselves in the competitive market, facilitates airlines to efficiently allocate resources, and empowers the research community with credible data for new discoveries. Existing clustering studies largely use traffic volume, network connectivity, or operational efficiency to group airports into hierarchical clusters or parallel partitions. Developing a different perspective, this study focuses upon a key bottleneck of outbound passenger movements in airport terminals, the security checkpoints, and uses the Transportation Security Administration (TSA) Customer Throughput/Wait Times Reports to cluster hub airports of the United States. With the input data structured as univariate time-series, k-shape, a clustering algorithm that is robust to time axis distortion and computationally efficient, is selected to analyze the similarity of time-series using shape-based distance. The clustering results are validated by examining the raw and z-normalized data of selected airport clusters on six sampled dates. Analysis results indicate that k-shape is competent and efficient to process and cluster time-series data used for this specific research. This study offers a fresh perspective to cluster commercial airports using an infrequently employed dataset. The clustering results reveal how the geographical location, hub status in airlines' operational network, and destination type of an airport affect the movement of outbound passengers through terminals     

Can freeway traffic volume information facilitate urban accessibility assessment?: Case study of the city of St. Louis

In urban contexts, accessibility measures are often utilized to represent the interactions among land use, transport and people’s trips to services and other facilities. Accessibility is generally evaluated using conventional traffic measures such as travel time and distance, but traffic volume, which is a product of the travel trips induced by people’s needs, may also be positively related to accessibility. This study tests the hypothesis “can freeway traffic volume information facilitate urban accessibility assessment?” Traffic volume based accessibility models are proposed to investigate this relationship and test the hypothesis. The results indicate that the answer to this question is a qualified “yes”, as there is a statistically significant relationship between traffic volume differences and accessibility. The coefficients of the models can potentially be used as a reference to guide the urban planning process. The limitations of the proposed models can be addressed by deploying Intelligent Transportation Systems (ITS) traffic sensors on ramps and arterial roadways.     

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.     

Bus quality partnerships,modal shift and traffic decongestion

A more sustainable transport system requires effective alternatives to private cars. Despite more than 40 years of declining use, buses are still the main form of local public transport outside central London. Government policy focuses upon Bus Quality Partnerships—agreements between highway authorities and bus operators to give bus priority access and invest in better quality buses—to reverse this decline and also attract car drivers to change modes and ease urban traffic congestion.This paper assesses the potential for Quality Partnerships to provide a more attractive bus service with the ability to achieve modal shift using a Greater Manchester case study. Preliminary results are presented from a comparative study of two Quality Bus Corridors (QBCs), one arterial route into Manchester’s Central Business District and one transverse from Leigh to Bolton. The research uses bus user interview surveys and in-depth interviews, which focus upon non-bus users.Results of this research show that Bus Quality Partnerships when introduced as a stand-alone policy struggle to achieve significant modal shift and traffic decongestion. Most bus passengers and car users remain unaware of Bus Quality Partnerships.     

Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001

Traditionally, the transport literature reflects the view that traffic volumes, road traffic volumes in particular, are coupled with Gross Domestic Product (GDP). Recently published literature also argues that the carbon dioxide (CO₂) emissions from transport, passenger cars in particular, have not shown any decoupling from transport volumes for some years. This article presents a theoretical framework for decoupling, defining the difference between decoupling, coupling and negative decoupling. These are further broken down to weak, strong and expansive/recessive degrees of decoupling, laying emphasis on the absolute increase or decrease of the variables. The result section presents data of the development of the relationships between GDP, traffic volumes and CO₂ emissions from transport in the EU15 countries between 1970 and 2001, including the special case of Finnish road traffic. The aggregate EU15 data show a change from expansive negative decoupling to expansive coupling regarding passenger transport, and from weak decoupling to expansive negative decoupling regarding freight transport. Weak decoupling of transport CO₂ emissions from GDP could also be observed. Weak decoupling of all the three aspects (freight, passenger and CO₂) could be seen in the UK, Sweden and Finland in the 1990s. In Finland, the statistics show weak decoupling of GDP from road traffic volume and strong decoupling of road traffic volume and CO₂ emissions from road traffic between 1990 and 2001. Four hypothetical explanations of the Finnish phenomenon are put forward in this article: policy towards sustainable mobility, green urban lifestyle, increasing income differences, and statistical misinterpretation. Each explanation is backed up with some quantitative evidence in observable trends in Finland during the 1990s.     

Regional public support to airlines and airports: An unsolved puzzle

This paper proposes a structural model to explain the motivation of regional public authorities to arrange marketing agreements for route and traffic development. Furthermore, using data from Spanish airports, we empirically test this model obtaining the demand function according to the preferences of public authorities. The results show that the public budget, airport’s attributes or intermodal competition affect to the demand for aircraft operations of regional public agencies. Finally, we propose an empirical method to determine the market power of airlines within these marketing agreements in a particular airport or route.     

Temporal aggregation and spatio-temporal traffic modeling

Traffic forecasting is crucial for policy making in the transport sector. Recently, Selby and Kockelman (2013) have proposed spatial interpolation techniques as suitable tools to forecast traffic at different locations. In this paper, we argue that an eventual source of uncertainty over those forecasts derives from temporal aggregation. However, we prove that the spatio-temporal correlation function is robust to temporal aggregations schemes when the covariance of traffic in different locations is separable in space and time. We prove empirically this result by conducting an extensive simulation study on the spatial structure of the Milan road network.     

Measuring the impact of traffic flow management on interarrival duration: An application of autoregressive conditional duration

The Federal Aviation Administration has several tools in its arsenal to manage traffic flows. However, it is very difficult to assess with certainty the impact of traffic flow management procedures such as Time-Based Flow Management (TBFM) or Traffic Management Initiatives (TMI) on airport performance because operational data are not readily available to analysts. This study uses the case of Fort Lauderdale–Hollywood International Airport (FLL) where traffic flow management procedures have been implemented to manage a reduction of airport capacity due to runway constructions. Based on an Autoregressive Conditional Duration (ACD) model, the analysis shows that the use of traffic flow management procedures contributed to reducing the volatility of interarrival duration whether separation relies on time-metering (TBFM) or distance between aircraft (TMI). The lessons learned from this case study may have important implications for airports whose available capacity is severely constrained.     

A service position model of package tour services based on the hybrid MCDM approach

ABSTRACT

Traditionally, package tour (PT) agencies assumed that tourists only pay attention to the PT’s price, so PT service providers often offer limited package tours based on options and prices. Nowadays, PT service providers face the difficult challenge of how to offer diverse PTs to satisfy tourists’ needs based on a reasonable price. This study proposes an evaluation model of package tours based on diverse resources and transportation means. This study proposes a two-dimensional evaluation framework to analyse tourists’ needs regarding package tour services and the proposed model could be applied to plan mass customized package tours based on the portfolio of value satisfaction index (VSI) and the price satisfaction index (PSI). The study also proposes that mass customized package tours can improve the traditional tour planning method through a portfolio of diverse tourism resources. Moreover, the study integrates the different choices of traffic and transportation tools, as well as tourism resources and providers in six portfolios of package tours in the Nantou county of Taiwan. These six package tours can be planned through three traffic and transportation tools (tourist coach, railroad, and minibus) and four tourism resources (natural experiences, cultural exploration, shopping and cuisine, and event participation).     

New potential hubs in the South-Atlantic market. A problem of location

Hub-and-spoke networks have become very popular in the airline industry. In this paper we study the potential hub candidates in the South-Atlantic market if a future process of liberalization creates a common air market area between South America and Europe. The analysis is based on the principal operating strategies that airlines consider when they choose their network configuration: central location in the market, local traffic at the hub cities and airport facilities. Traffic levels, great circle distances between the airports and other variables are used to analyze possible differences among airports. Other implications concerning the agents involved like gains in frequencies, time penalties and burden of capacity are also explored. Results of the analysis are used to extract some policy considerations and to stimulate discussion by government officials, air industry analysts and academics, about the necessity of the introduction of more liberalized measures in emergent markets.     

Determinants of route choice behavior: A comparison of shop versus work trips using the Potential Path Area - Gateway (PPAG) algorithm and Path-Size Logit

This paper presents an in-depth comparison of route choice models for work and shop vehicle trips—with emphasis on the interactions between route attributes and individual characteristics—to better understand the route choice determinants that are assumed to vary by trip purpose. Insights into the route choice behavior involving two dominant vehicle trip purposes—work and shop trips—will help in the design of traffic facilities and implementation of measures to influence route choice in the desired direction.In this study, we show that the utility and scale parameters for separate models of work and shop trips differ by direct comparison using a sequential scaling estimation method and likelihood ratio tests, and highlight the differences in route choice behavior by considering the interaction of route attributes and individual characteristics using Path-Size Logit modeling. In the process, we used Potential Path Area - Gateway (PPAG) algorithm—that generates feasible route choice sets for route choice modeling from GPS trajectories of observed routes.The results show that, indeed, route choice behavior varies by trip, which suggests that drivers attach value to route choice determinants relative to trip purpose. The inclusion of interaction terms in model specifications further indicates that work route choice behavior tends to be restrictive compared to the nonrestrictive route choice for shop trips—a generalization consistent with the mandatory and discretionary nature of work and shop trips, respectively. Specifically, individual characteristics such as personal income, age, gender, tenure, household size, and access to public transit affect route choice behavior.     

Recreational traffic management: The relations between research and implementation

Traffic management in National Parks is necessary in order to avoid the negative effects of car traffic. Implementing traffic management measures can be difficult, due to public resistance or other problems. A research project in the Veluwezoom National Park in The Netherlands shows that there are two important reasons for problematic implementation. The first reason is a lack of knowledge about the recreational use of the area. The second reason is the interdependence of actors involved. Both aspects need to be considered and related to each other in order to make more realistic traffic management plans.     

On constituting cyclists as ‘hazards’

‘Hazard perception’ has become an integral part of novice driver education and training. Cyclists are often identified as one of many ‘hazards’ to look out for. We speculate that constituting cyclists as ‘hazards’, something that presents a danger or threat, may foster negative attitudes toward cyclists. Rather than accepting cyclists as ‘hazards’, our study examined the conditions that have made it possible to identify cyclists as ‘hazards’ in novice driver preparation. Informed by Michel Foucault's work on discursive practices, the analysis focused on the ‘road safety’ literature (1900–2017), the changing context in which road safety knowledge has been produced and the implications for the production of road space. This literature is important given the authority of scientific knowledge in western societies and its role in managing and governing populations. We found a shift in the middle of the twentieth century from drivers being identified as ‘hazards’ to drivers being identified as perceivers of ‘hazards’. At this time, researchers began studying drivers for their ability to recognise hazards: cyclists were routinely listed among the ‘hazards’ drivers should perceive. Out of 200 articles published on drivers' ‘hazard perception’ since the 1960s, one third categorised cyclists as ‘hazards’. Such research has informed the development and implementation of ‘hazard perception’ tests and, following Foucault, it participates in producing road space and shaping how drivers can think about themselves and other road users. While ‘scientific’ studies constitute cyclists as potential threats or sources of harm they lend authority to negative views of cyclists. We suggest ‘traffic participation’ as a more inclusive approach to driver education and training.     

Airport ground access and private car use: a segmentation analysis

The increasing scale of, and demand for, civil air transport world-wide has resulted in a greater volume of people travelling to and from airports. The vast majority of these journeys are made by private cars, which has led to traffic congestion and raised levels of air pollution in and around airports. Subsequently, airports are re-evaluating their approach to ground access mode choice and considering how to reduce the reliance on private cars. Based on a survey of passengers at Manchester Airport in the UK, attitude statements pertaining to psychological constructs from the Theory of Planned Behaviour and the Norm-Activation Theory, combined with key factors relating to the passenger’s trip, are used to identify eight behaviourally distinct groups of passengers with varying potential to reduce their private car use. Two of these groups, described as the Conflicted Greens and the Pessimistic Lift Seekers, are identified as having the greatest potential to reduce private car use to airports. Analysis reveals the need for decision makers to address the existing attitude–behaviour ‘gap’ that can prevent positive environmental attitudes being translated into the use of more sustainable modes, as well as tackling the perceived difficulty some passengers associate with using these modes.