Throwing space back in: a tale of Indian fishermen,ICT and travel behavior

A suite of agent-based models is designed seeking to replicate and expand the findings of a case study examining the effects of introducing cell phones in a regional fisheries market in southern India. The remote availability of market price information led to economic benefits but also longer average travel distances for the fishermen. Our work generalizes these findings, highlighting the role of space and developing scenarios that involve different configurations for the markets and different assumptions about information-gathering strategies. Here we focus on the development of the model as a tool for exploring behavioral adaptations of ICT-using, utility-oriented travelers in different spatio-temporal contexts.     

Regional transport and its association with tuberculosis in the Shandong province of China, 2009–2011

Human mobility has played a major role in the spread of infectious diseases such as tuberculosis (TB) through transportation; however, its pattern and mechanism have remained unclear. This study used transport networks as a proxy for human mobility to generate the spatial process of TB incidence. It examined the association between TB incidence and four types of transport networks at the provincial level: provincial roads, national roads, highways, and railways. Geographical information systems and geospatial analysis were used to examine the spatial distribution of 2217 smear-positive TB cases reported between 2009 and 2011 in the Shandong province. The study involved factors such as population density and elevation difference in conjunction with the types of transport networks to predict the disease occurrence in space. It identified spatial clusters of TB incidence linked not only with transport networks of the regions but also differentiated by elevation. Our research findings provide evidence of targeting populous regions with well-connected transport networks for effective surveillance and control of TB transmission in Shandong.     

Assessment of non-recurrent traffic congestion caused by freeway work zones and its statistical analysis with unobserved heterogeneity

Freeway work zones with patching, paving, lane marking, debris removing, and weeding cause temporary capacity reduction in the freeway and may lead to non-recurrent traffic congestion. Such non-recurrent traffic congestion amounts to 10% of total traffic congestion in the U.S. and 31% in Germany. Non-recurrent traffic congestion has been estimated by using the capacity and the number of closed lanes in work zones and the upstream traffic demand of work zones. However, the number of the closed lanes may be insignificant due to operational strategies such as using the shoulder area and composing additional lanes by temporarily reducing the existing lane width to mitigate traffic congestion. Therefore, the objective of this study is to develop a method to quantify non-recurrent traffic congestion caused by freeway work zones based on traffic flow data and spatio-temporal work zone information. In addition, to demonstrate the efficacy of the developed method, a case study is conducted based on one-year historical traffic data and work zone data on major freeways in Korea. Then, multivariate statistical analysis with unobserved heterogeneity is performed to describe factors of non-recurrent traffic congestion caused by work zone activities. Due to the fact that a work zone project is usually implemented according to schedule, such negative impact as non-recurrent traffic congestion is inevitably produced. Thus, the results can be practical for the performance evaluation of congestion management programs for work zone by quantifying non-recurrent traffic congestion. Additionally, the results from the statistical analysis can be potentially useful in developing a forecasting model for providing travelers with traffic information such as an alternative route to escape non-recurrent traffic congestion by freeway work zones.     

Predicting the distribution of households and employment: a seemingly unrelated regression model with two spatial processes

Household and employment counts (by type) are key inputs to models of travel demand and air quality. For a variety of reasons, spatial dependence is very likely present in and across these counts. In order to identify the nature of these unobserved relationships, this study provides the first application of a feasible generalized spatial 3SLS estimation procedure for a seemingly unrelated regression (SUR) model with two spatial processes. Statistical tests reveal that this more generalized model is superior to its constrained versions (e.g., SUR models without spatial components or with just a spatial lag or spatial error process).In the resulting model of Austin, Texas data, local land-use conditions offer substantial predictive power of household and job densities, and transportation access plays a role, as anticipated. The work demonstrates that SUR estimation of land-use intensities from parcel-level data with two types of spatial dependence is feasible and meaningful. Coupled with an upstream model of land-use type, this work offers key inputs for travel demand analyses, with transportation system performance feedback.     

Work,travel and home: a study of remembrance activity

A quantitative study conducted in the Australian regional city of Ballarat resulted in a sample which had a high proportion of people with a personal connection to war and remembrance through family. This connection was reflected in higher levels of visitation to local, state and overseas war memorials. A factor analysis suggested that some kinds of remembrance could be grouped into a three part structure based upon creative activities of Work such as writing history, volunteer and paid military work and collecting, Travel to overseas and domestic memorials and informal appreciation of artefacts at Home. The Home group represents the most frequent form of remembrance, practiced at a social scale and which results from the creative activity of individuals. The study therefore supports the notion that individual and social remembrance and memory are closely linked and can be identified with patterns of travel. A potentially large group of people who appeared to have little interest in war remembrance was also identified.     

Dependence between travel distance,individual socioeconomic and health-related characteristics,and the choice of the travel mode: a cross-sectional study for Kaunas,Lithuania

The aim of this study was to analyse the associations between individual socioeconomic and health-related characteristics, travel distance, and the choice of different travel modes in urban population. A cross-sectional study included 932 adults of Kaunas city, Lithuania. The choice of the travel mode and individual characteristics were self-reported by the participants, and their travel routes were calculated using the geographic information system. Multivariate logistic regression was used to assess the most significant factors determining the choice of a car, cycling, walking, or public transport. In total, 529 participants reported using a car, of whom 65.8% had medium or high education levels. These participants were more likely to be younger, male, married, and employed. Among bicycle users, statistically significant differences between the employment status, body mass index, and travel distance were observed. Walkers were significantly more likely to be older, those with lower incomes, unemployed, and travelling the shortest distances. The analysis of the travel distance on the choice of the travel mode revealed that men travelled longer distances with a car compared to women. The employment status was significantly associated with travel distance by car or public transport. Employed individuals travelled longer distances by public transport or by car, compared to unemployed individuals. Among bicycle users, we found that people with higher levels of education and overweight individuals cycled the longest distances. Our study emphasizes the importance of considering different individual characteristics when analysing the choice of transport modes. It provides evidence that is relevant for all urban populations on the choice of the transport mode, particularly considering active versus passive transport.     

Public bicycle as a feeder mode to rail transit in China: The role of gender,age, income,trip purpose,and bicycle theft experience

A marriage between public bicycle and rail transit presents new opportunities for sustainable transportation in Chinese cities. To examine determinants of public bicycle usage for rail transit access, an intercept survey of feeder mode choice among rail transit users was conducted near rail stations in Nanjing, China. Mode choice models were estimated with five feeder mode alternatives, including car, bus, walk, private bike, and public bike. By differentiating between public and private bicycle modes in the mode choice models, the study reveals the effects of personal demographics, trip characteristics, and station environments on public bicycle usage for rail transit access. Results show that female, older, and low-income rail commuters are less likely to use public bicycle to access rail transit. Rail commuters with bicycle theft experience and making school- or work-related trips are more likely to use public bicycle to access rail transit. Land use variables are largely insignificant in this study except that density shows a positive relationship with walking to rail transit. The results on demographic differences raise equity concerns when it comes to investing in public bicycle systems. Policy implications are discussed for Chinese cities to equitably boost public bicycle integration with rail transit.     

A geographically weighted regression model to examine the spatial variation of the socioeconomic and land-use factors associated with Strava bike activity in Austin,Texas

Despite evidence showing the spatial nonstationarity of the determinants of bike activity, very few studies have addressed the phenomena, probably due to the limited sample size of the traditional count data. To address this gap, this study demonstrated the applicability of Strava bike activity data by developing a geographically weighted Poisson regression (GWPR) model that can reveal how the influence of socioeconomic and land-use factors vary across a region. The city of Austin was selected as a case study, and Strava bike volume was gleaned from 1494 intersections. The representativeness of the Strava data was first examined by comparing those data with the video-based actual bicycle volume data from 43 intersections in the study area. Despite the high deviation in several locations, Strava volume exhibited moderate linear relationships with actual volume. The GWPR model developed in this study outperformed the traditional global model and revealed significant spatial variability of nine variables related to age, income, education, transit stops, hub locations, offices, schools, trails, and sidewalk facilities. Notable spatial variations on bike activity were observed across the study area in terms of magnitude, direction, and significance of the impact for all model variables. The analysis and discussion offer guidance to practitioners and policy makers in tailoring policies and programs that consider the spatial context. The study also provides insights for understanding the potential use of crowdsourced data in examining bike activity, especially when resources are limited.