Spatiotemporal influence of land use and household properties on automobile travel demand

Understanding the patterns of automobile travel demand can help formulate policies to alleviate congestion and pollution. This study focuses on the influence of land use and household properties on automobile travel demand. Car license plate recognition (CLPR) data, point-of-interest (POI) data, and housing information data were utilized to obtain automobile travel demand along with the land use and household properties. A geographically and temporally weighted regression (GTWR) model was adopted to deal with both the spatial and temporal heterogeneity of travel demand. The spatial-temporal patterns of GTWR coefficients were analyzed. Also, comparative analyses were carried out between automobile and total person travel demand, and among travel demand of taxis, heavily-used private cars, and total automobiles. The results show that: (I) The GTWR model has significantly higher accuracy compared with the Ordinary Least Square (OLS) model and the Geographically Weighted Regression (GWR) model, which means the GTWR model can measure both the spatial and temporal heterogeneity with high precision; (II) The influence of built environment and household properties on automobile travel demand varies with space and time. In particular, the temporal distribution of regression coefficients shows significant peak phenomenon; and (III) Comparative analyses indicate that residents’ preference for automobiles over other travel modes varies with their travel purpose and destination. The above findings indicate that the proposed method can not only model spatial-temporal heterogeneous travel demand, but also provide a way to analyze the patterns of automobile travel demand.     

Autonomous cars: The tension between occupant experience and intersection capacity

Systems that enable high levels of vehicle-automation are now beginning to enter the commercial marketplace. Road vehicles capable of operating independently of real-time human control under an increasing set of circumstances will likely become more widely available in the near future. Such vehicles are expected to bring a variety of benefits. Two such anticipated advantages (relative to human-driver vehicle control) are said to be increased road network capacity and the freeing up of the driver-occupant’s time to engage in their choice of leisurely or economically-productive (non-driving) tasks.In this study we investigate the implications for intersection capacity and level-of-service of providing occupants of automated (without real-time human control), autonomously-operating (without vehicle-to-X communication) cars with ride quality that is equivalent (in terms of maximum rates of longitudinal and lateral acceleration) to two types of rail systems: [urban] light rail transit and [inter-urban] high-speed rail. The literature suggests that car passengers start experiencing discomfort at lower rates of acceleration than car drivers; it is therefore plausible that occupants of an autonomously-operating vehicle may wish to instruct their vehicle to maneuver in a way that provides them greater ride comfort than if the vehicle-control algorithm simply mimicked human-driving-operation.On the basis of traffic microsimulation analysis, we found that restricting the dynamics of autonomous cars to the acceleration/deceleration characteristics of both rail systems leads to reductions in a signalized intersection’s vehicle-processing capacity and increases in delay. The impacts were found to be larger when constraining the autonomous cars’ dynamics to the more-restrictive acceleration/deceleration profile of high-speed rail. The scenarios we analyzed must be viewed as boundary conditions, because autonomous cars’ dynamics were by definition never allowed to exceed the acceleration/deceleration constraints of the rail systems. Appropriate evidence regarding motorists’ preferences does not exist at present; establishing these preferences is an important item for the future research agenda.This paper concludes with a brief discussion of research needs to advance this line of inquiry.     

Flying with climate liability? Economic valuation of voluntary carbon offsets using forced choices

This study aims to examine how key aspects of voluntary climate action influence economic values of aviation carbon offsets using an Australian case study, where voluntary carbon offset programs for the aviation sector were active under a carbon tax between July 01, 2012 and July 17, 2014. An online survey was administered during the period using choice experiments. This rare and short-lived Australian experience is useful to gain insights into how individuals respond to the new public policy in terms of the perceived economic value of voluntary offsets for air travel. According to the estimation results, supporters of the mandatory tax policy held a welfare value of voluntary carbon offsets for their domestic flights that is three times larger than non-supporters (i.e., $AU27.83 vs. $AU9.40). It is $AU12.27 on average per ton of carbon offsets per person for domestic flights and $AU0.92 for international long-haul flights. The findings endorse that individuals seem to attach personal responsibility for carbon emissions (i.e. climate liability or carbon conscience) to frequent domestic flights, but not so much to intercontinental flights. Furthermore, reported flight frequencies by respondents did not place any significant impact on economic values of voluntary carbon offsets in both domestic and international frameworks. A coupled approach between forced choices and certainty responses was adapted, where no-choice options were retrieved, potentially improving choice experiments. Results suggest that airlines should consider simplifying their carbon offset programs to fixed levels (e.g. £3, £10, and £20 as in the case of British Airways), regardless of geographical boundaries, while governments should promote both mandatory and voluntary climate measures in tandem.     

Development and calibration of a long-distance passenger traffic assignment model

This paper studies the assignment of long-distance passenger traffic on a highway corridor network. First, we propose a traditional model for the long-distance traffic assignment considering interactions with local commuter traffic. It addresses the effect of local networks on highway corridors. An iterative algorithm is developed to solve for the exact solution. Then, to address the potential computational issues that arise therein, a decomposition method is proposed by introducing a new concept of corridor elasticity. An assignment procedure for long-distance passenger traffic is developed accordingly. Numerical tests show that the proposed decomposition method makes significant improvements in computational performance at a small loss of optimality. This decomposition method well approximates the exact assignment from the traditional formulation, especially when the highway corridors are near-saturation. The proposed decomposition method appears practical for application.     

Potentials of using social media to infer the longitudinal travel behavior: A sequential model-based clustering method

This study explores the possibility of employing social media data to infer the longitudinal travel behavior. The geo-tagged social media data show some unique features including location-aggregated features, distance-separated features, and Gaussian distributed features. Compared to conventional household travel survey, social media data is less expensive, easier to obtain and the most importantly can monitor the individual’s longitudinal travel behavior features over a much longer observation period. This paper proposes a sequential model-based clustering method to group the high-resolution Twitter locations and extract the Twitter displacements. Further, this study details the unique features of displacements extracted from Twitter including the demographics of Twitter user, as well as the advantages and limitations. The results are even compared with those from traditional household travel survey, showing promises in using displacement distribution, length, duration and start time to infer individual’s travel behavior. On this basis, one can also see the potential of employing social media to infer longitudinal travel behavior, as well as a large quantity of short-distance Twitter displacements. The results will supplement the traditional travel survey and support travel behavior modeling in a metropolitan area.     

The desulphurisation of shipping: Past,present and the future under a global cap

As from January 2020, the International Maritime Organization (IMO) is implementing a global 0.5% limit on the sulphur content of fuel, commonly known as the global sulphur cap. This limit is the latest policy in the efforts to reduce sulphur emissions from shipping, following the designation of emission control areas (ECAs) and other regional regulations. In this paper, a literature review is conducted of academic studies that have dealt with issues relating to the reduction of maritime sulphur emissions. Various recurring research themes are identified, spanning the areas of operations research, maritime economics and transport policy. The effects and implications of available compliance options are then analyzed from the perspectives of ship operators, shippers and consumers. Using lessons learned from the enforcement of ECA regulations, this is followed by an appraisal of various potential issues related to the enforcement of these new global regulations. It is found that a homogeneous enforcement regime is required to ensure a level playing field amongst ship operators and that the global sulphur cap may lead to serious market distortion, due to the potential short term rise of fuel prices. The paper concludes with a set of recommendations for future research on sulphur emissions from shipping in the aftermath of the global cap and, looking forward, to its relationship to the IMO strategy on the reduction of greenhouse gas (GHG) shipping emissions.     

Solving the gate assignment problem through the Fuzzy Bee Colony Optimization

In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of solving high-level combinatorial problems, like the Flight-Gate Assignment Problem (FGAP), with fast convergence performances. However, given that the FGAP can be often affected by uncertainty or approximation in data, in this paper we develop a new metaheuristic algorithm, based on the Fuzzy Bee Colony Optimization (FBCO), which integrates the concepts of BCO with a Fuzzy Inference System. The proposed method assigns, through the multicriteria analysis, airport gates to scheduled flights based on both passengers’ total walking distance and use of remote gates, to find an optimal flight-to-gate assignment for a given schedule. Comparison of the results with the schedules of real airports has allowed us to show the characteristics of the proposed concepts and, at the same time, it stressed the effectiveness of the proposed method.     

基于家庭属性差异的大学生出行方式选择行为研究

为剖析家庭属性差异对大学生出行方式选择行为的影响，基于非集计理论，构建家庭属性差异的大学生出行选择多元Logit 模型. 根据四川省2 571 份大学生出行行为调查问卷，运用SPSS 软件标定模型参数，获取影响大学生出行选择的主要家庭属性因素，并进行敏感性分析. 结果表明：家庭平均年收入、经济净流对大学生出行方式选择有显著的影响；以航空运输为参考，家庭平均年收入、经济净流对公路运输方式选择的影响大于铁路运输；“祖辈替孙辈购买机票”的折扣票务形式可提高大学生选择航空出行的概率.     

The potential mitigation of CO2 emissions via modal substitution of high-speed rail for short-haul air travel from a life cycle perspective – An Australian case study

The objective of this study is to provide a strategic evaluation of the mitigation of CO₂ emissions via modal substitution of high-speed rail for short-haul air travel on the Sydney–Melbourne, Australia city-pair from a life cycle perspective. It has been demonstrated that when considering CO₂ emissions from vehicle operations, the modal shift from air to high-speed rail on this city-pair has the potential to provide a means of CO₂ mitigation. However, uncertainty exists with regard to the level of mitigation potential when considering the whole-of-life performance of the systems. Given the significant difference in the infrastructure requirements between the air mode and the high-speed rail mode, this study quantifies the life cycle CO₂ load attributable to each system and examines the effect on CO₂ mitigation potential. The study concluded that while the inclusion of the linehaul infrastructure did increase the CO₂ load associated with high-speed rail mode, it did not equate to or exceed the CO₂ load per trip as experienced by the air mode. The avoided annual life cycle CO₂ emission in the target year 2056 was 0.37 Mt representing an 18% reduction when compared to the air mode only on the city pair. In fact, the scenario comparison indicated that the substitution of high-speed rail for short-haul air travel on the city pair resulted in CO₂ emissions avoidance throughout the longitudinal period.     

Agent-based simulation framework for mixed traffic of cars,pedestrians and trams

In this paper, we report on the construction of a new framework for simulating mixed traffic consisting of cars, trams, and pedestrians that can be used to support discussions about road management, signal control, and public transit. Specifically, a layered road structure that was designed for car traffic simulations was extended to interact with an existing one-dimensional (1D) car-following model and a two-dimensional (2D) discrete choice model for pedestrians. The car model, pedestrian model, and interaction rules implemented in the proposed framework were verified through simulations involving simple road environments. The resulting simulated values were in near agreement with the empirical data. We then used the proposed framework to assess the impact of a tramway extension plan for a real city. The simulation results showed that the impact of the proposed tramway on existing car traffic would not be serious, and by extension, implied that the proposed framework could help stakeholders decide on expansion scenarios that are satisfactory to both tram users and private car owners.