Empirical distributions of vehicle use and fuel efficiency across space: Implications of asymmetry for measuring policy incidence

Concerns about local air pollution and climate change have prompted all levels of government to consider a variety of policies to reduce vehicle dependence and fuel consumption, as the transportation sector is one of the largest sources of local and global emissions. Because many of the policy options under consideration are market-based (e.g., gasoline tax, carbon tax), it is important to consider how the impacts would vary across space and affect different subpopulations. Evaluating incidence is relevant for both the expected costs and benefits of a particular policy, however detailed data on vehicle-miles traveled (VMT) and fuel consumption allowing for the distributions of these variables to be estimated at a fine geographic scale is rarely available. This paper uses a unique dataset with more than 20 million vehicles in California to derive estimates of VMT and fuel consumption in order to examine the spatial distribution of impacts for an increase in the price of gasoline as well as the consequences of using different statistics for policy evaluation. Results show that VMT and fuel consumption distributions are not symmetrically distributed and vary significantly within transportation planning regions. To understand the potential implications of this asymmetry, we do a back of the envelope comparison using the mean and mode of the VMT or fuel consumption distribution for policy analysis. We find that assuming a symmetric distribution can lead to a divergence of 20–40% from the estimates based on the empirical distribution. Our results, therefore, introduce caution in interpreting the incidence of policies targeting the transportation sector based on averages.     

Analyzing spatiotemporal traffic line source emissions based on massive didi online car-hailing service data

Nowadays, the massive car-hailing data has become a popular source for analyzing traffic operation and road congestion status, which unfortunately has seldom been extended to capture detailed on-road traffic emissions. This study aims to investigate the relationship between road traffic emissions and the related built environment factors, as well as land uses. The Computer Program to Calculate Emissions from Road Transport (COPERT) model from European Environment Agency (EEA) was introduced to estimate the 24-h NOx emission pattern of road segments with the parameters extracted from Didi massive trajectory data. Then, the temporal Fuzzy C-Means (FCM) Clustering was used to classify road segments based on the 24-h emission rates, while Geographical Detector and MORAN’s I were introduced to verify the impact of built environment on line source emissions and the similarity of emissions generated from the nearby road segments. As a result, the spatial autoregressive moving average (SARMA) regression model was incorporated to assess the impact of selected built environment factors on the road segment emission rate based on the probabilistic results from FCM. It was found that short road length, being close to city center, high density of bus stations, more ramps nearby and high proportion of residential or commercial land would substantially increase the emission rate. Finally, the 24-h atmospheric NO₂ concentrations were obtained from the environmental monitor stations, to calculate the time variational trend by comparing with the line source traffic emissions, which to some extent explains the contribution of on-road traffic to the overall atmospheric pollution. Result of this study could guide urban planning, so as to avoid transportation related built environment attributes which may contribute to serious atmospheric environment pollutions.     

Changes to commute mode: The role of life events,spatial context and environmental attitude

It has been suggested that commuting behaviours become habitual and that changes to commute mode are more likely at the time of major life events. However, evidence to support this has so far been limited to analyses of small-scale samples. To address this evidence gap, we use two waves of panel data from the UK Household Longitudinal Study (2009/10 and 2010/11) to identify and explain the prevalence of individual change in commute mode from year to year amongst a representative sample of the English working population (n = 15,200). One third of those that cycle or get the bus to work, and one quarter of those that walk to work, are shown to change commuting mode by the following year. Car commuting is more stable, with only one in ten car commuters changing mode by the following year. Commute mode changes are found to be primarily driven by alterations to the distance to work which occur in association with changing job or moving home. Switching to non-car commuting becomes much more likely (9.2 times) as the distance to work drops below three miles. High quality public transport links to employment centres are shown to encourage switches away from car commuting and mixed land uses are shown to encourage switches to active commuting (walking and cycling). Switches away from car commuting are found to be more likely (1.3 times) for those with a pro-environmental attitude. The attitude orientation is shown to precede the behaviour change, demonstrating evidence of ‘cause and effect’. Overall, the study shows that changes in commuting behaviour are strongly influenced by life events, spatial context and environmental attitude.     

A range-restricted recharging station coverage model for drone delivery service planning

Unmanned Aerial Vehicles (UAVs) are attracting significant interest for delivery service of small packages in urban areas. The limited flight range of electric drones powered by batteries or fuel cells requires refueling or recharging stations for extending coverage to a wider area. To develop such service, optimization methods are needed for designing a network of station locations and delivery routes. Unlike ground-transportation modes, however, UAVs do not follow a fixed network but rather can fly directly through continuous space. But, paths must avoid barriers and other obstacles. In this paper, we propose a new location model to support spatially configuring a system of recharging stations for commercial drone delivery service, drawing on literature from planar-space routing, range-restricted flow-refueling location, and maximal coverage location. We present a mixed-integer programming formulation and an efficient heuristic algorithm, along with results for a large case study of Phoenix, AZ to demonstrate the effectiveness and efficiency of the model.     

Influences of built environment characteristics and individual factors on commuting distance: A multilevel mixture hazard modeling approach

Concerns over transportation energy consumption and green-household gas (GHG) emissions have prompted a growing body of research into the influence of built environment on travel behavior. Studies on the relationship between land use and travel behavior are often at a certain aggregated spatial unit such as traffic analysis zone (TAZ), spatial issues occur among individuals clustered within a zone because of the locational effects. However, recognition of the spatial issues in travel modeling was not sufficiently investigated yet. The object of this study is twofold. First, a multilevel hazard model was applied to accommodate the spatial context in which individuals generate commuting distance. Second, this research provides additional insights into examine the effects of socio-demographics and built environment on commuting distance. Using Washington metropolitan area as the case, the built environment measures were calculated for each TAZ. To estimate the model parameters, the robust maximum likelihood estimation method for a partial function was used, and the model results confirmed the important roles that played by the TAZ and individual level factors in influencing commuting distance. Meanwhile, a comparison among the general multilevel model, single level and multilevel hazard models was conducted. The results suggest that application of the multilevel hazard-based model obtains significant improvements over traditional model. The significant spatial heterogeneity parameter indicates that it is necessary to accommodate the spatial issues in the context of commuting distance. The results are expected to give urban planners a better understanding on how the TAZ and individual level factors influence the commuting distance, and consequently develop targeted countermeasures.     

What determines rail transit passenger volume? Implications for transit oriented development planning

Transit oriented development (TOD) has been an important topic for urban transportation planning research and practice. This paper is aimed at empirically examining the effect of rail transit station-based TOD on daily station passenger volume. Using integrated circuit (IC) card data on metro passenger volumes and cellular signaling data on the spatial distribution of human activities in Shanghai, the research identifies variations in ridership among rail transit stations. Then, regression analysis is performed using passenger volume in each station as the dependent variable. Explanatory variables include station area employment and population, residents’ commuting distances, metro network accessibility, status as interchange station, and coupling with commercial activity centers. The main findings are: (1) Passenger volume is positively associated with employment density and residents’ commuting distance around station; (2) stations with earlier opening dates and serving as transfer nodes tend to have positive association with passenger volumes; (3) metro stations better integrated with nearby commercial development tend to have larger passenger volumes. Several implications are drawn for TOD planning: (1) TOD planning should be integrated with rail transit network planning; (2) location of metro stations should be coupled with commercial development; (3) high employment densities should be especially encouraged as a key TOD feature; and (4) interchange stations should be more strategically positioned in the planning for rail transit network.     

A comparative analysis of transportation-based accessibility to mental health services

The demand for mental health services has been growing stronger over the last couple of decades. This indicates the need to study and assess the access to these mental health services especially with a focus on the vulnerable populations having the greatest need. As such, this paper presents a Geographical Information Systems (GIS)-based analysis in order to study and evaluate the accessibility of mental health facilities using the information on the spatial distributions of population and facilities, and regional traffic characteristics. For this purpose, different age group segments are utilized including the total population as well as those aged between 18 and 21, 22 and 49, 50 and 64, and those aged over 65 and 85. Focusing on the State of Florida, spatially detailed accessibility metrics are calculated with regard to healthcare facilities using travel times between population block groups and these critical mental health facilities. These estimates are used to calculate the weighted county accessibility scores for each county. Findings clearly delineate those counties that lack access to mental facilities, especially those in Northwest Florida, a demographically diverse and substantially rural region. This type of analysis can help planners and policy makers develop better strategies in order to provide adequate mental health care options needed in targeted locations.     

Using a k-means clustering algorithm to examine patterns of pedestrian involved crashes in Honolulu,Hawaii

The purpose of this paper is twofold: 1) to describe a statistical technique known as K-means clustering in term of its advantages and disadvantages in safety research; and, 2) to use this method to analyze spatial patterns of pedestrian-involved crashes in Honolulu. K-means, a partitioning clustering technique, provides a powerful tool for analyzing and visualizing spatial patterns. While there are other techniques, one of the advantages of the K-means approach is that it is a well established technique that has been used for many different applications other than traffic safety. In this paper, we compare it to hierarchical clustering techniques and suggest that both are useful in the arsenal of spatial analytic tools for safety research.     

A spatial decision support system for retail plan generation and impact assessment

Current geographic information systems typically offer limited analytical capabilities and lack the flexibility to support spatial decision making effectively. Spatial decision support systems aim to fill this gap. Following this approach, this paper describes an operational system for integrated land-use and transportation planning called Location Planner. The system integrates a wide variety of spatial models in a flexible and easy-to-use problem solving environment. Users are able to construct a model out of available components and use the model for impact analysis and optimization. Thus, in contrast to existing spatial decision support systems, the proposed system allows users to address a wide range of problems. The paper describes the architecture of the system and an illustrative application. Furthermore, the potentials of the system for land-use and transportation planning are discussed.     

符夹铁路青龙山特大桥空间刚架设计

小角度交叉桥梁结构中,空间框架结构具有结构高度低、整体刚度大、施工难度小、侧墙可打造多种景观需求等优点。符夹铁路青龙山特大桥地处中软土地基,小角度斜向上跨青符铁路联络线和两个下穿涵洞,对桥下通行净空和基础位置设置有较高要求。为了合理设计大桥,根据桥梁基本情况进行桥型方案比选,并对基础间断布置的空间刚架方案进行设计计算论证。结果表明,所选结构形式合理且为传统刚架结构提供新的扩展形式。