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.     

The determinants of long distance travel in Great Britain

This study analyses of the determinants of long distance travel in Great Britain using data from the 1995-2006 National Travel Surveys (NTSs). The main objective is to determine the effects of socio-economic, demographic and geographic factors on long distance travel. The estimated models express the distance travelled for long distance journeys as a function of income, gender, age, employment status, household characteristics, area of residence, size of municipality, type of residence and length of time living in the area. A time trend is also included to capture common changes in long distance travel over time not included in the explanatory variables. Separate models are estimated for total travel, travel by each of four modes (car, rail, coach and air), travel by five purposes (business, commuting, leisure, holiday and visiting friends and relatives (VFRs)) and two journey lengths (<150 miles and 150+ miles one way), as well as the 35 mode-purpose-distance combinations.The results show that long distance travel is strongly related to income: air is most income-elastic, followed by rail, car and finally coach. This is the case for most journey purposes and distance bands. Notable is the substantial difference in income elasticities for rail for business/commuting as opposed to holiday/leisure/VFR. In addition, the income elasticity for coach travel is very low, and zero for the majority of purpose-distance bands, suggesting coach travel to be an inferior mode in comparison to car, rail and air. Regarding journey distance, we find that longer distance journeys are more income elastic than shorter journeys.For total long distance travel, the study indicates that women travel less than men, the elderly less than younger people, the employed and students more than others, those in one adult households more than those in larger households and those in households with children less than those without. Long distance travel is also lowest for individuals living in London and greatest for those in the South West, and increases as the size of the municipality declines.     

Daily activity and travel sequences of students,faculty and staff at a large Canadian university

This paper examines the activity engagement, sequencing and timing of activities for student, faculty and staff commuter groups at the largest university in the Maritime Provinces of Canada. The daily activity patterns of all university community groups are modeled using the classification and regression tree classifier algorithm. The data used for this study are derived from the Environmentally Aware Travel Diary Survey (EnACT) conducted in spring 2016 at Dalhousie University, Nova Scotia. Results show that there are significant differences in activity and travel behavior between university population segments and the general population in the region, and between campus groups. For example, students participate in more recreation activities compared to faculty and staff. They also take more trips to and from campus, and are more flexible in their scheduling of trips. The insights gained from this study will provide helpful information for promoting sustainability across university campuses, and for development of campus-based travel demand management strategies.     

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.     

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.     

北京市旅游交通方式选择行为研究

为防止北京市旅游高峰期间景区周边交通混乱以及城市交通路网负荷增加，选取北京市三 处景点进行问卷调查，将游客的年龄、是否有车、月收入、同行人数及同伴类型等数据作为影响游客交通方式选择的因素。与此同时，对所需数据进行有效性检验，进而分析不同游客的出行方 式特性，利用非集计模型对游客的交通方式选择行为进行研究。结果显示：游客的年龄、是否有车、月收入、同行人数以及同伴类型这几类因素对游客的交通方式选择的影响均显著，并得到游 客对私家车、出租车、地铁及公交车这四类交通方式选择的比例，分别为17%, 7%, 54%, 22%。研究表明，北京市旅游交通方式的需求以公共交通为主，尤其是对轨道交通的需求，为此建议进 一步加大北京市旅游公共交通系统建设，提高旅游交通的服务质量。     

基于混合选择模型的汽车共享选择行为研究

为了深入分析出行者的汽车共享选择行为,首先以技术接受模型和计划行为理论为框架,将对汽车共享选择行为具有影响的心理因素整合到传统的离散选择模型之中,形成混合选择模型.然后,基于南京市的实证调查数据,运用混合选择模型对出行者的汽车共享选择行为进行研究.结果表明,出行者对于汽车共享的感知有用性、感知易用性、行为态度、知觉行为控制等心理态度潜变量对其选择行为产生显著的正向影响,混合选择模型比传统不带潜变量MNL模型对实证数据具有更高的拟合度.