Urban freight,parking and pricing policies: An evaluation from a transport providers’ perspective

This paper investigates transport providers’ preferences for alternative loading bays and pricing policies. It estimates the importance of loading bays, the probability of finding them free and offers strategically relevant information to policy makers. The results underline the relevance of both preference heterogeneity and non-linear attribute effects. Three classes of agents are detected with substantially different preferences also characterized by non-linear sensitivity to attribute level variations. The specific freight sector, frequency of accesses and number of employees are all relevant covariates explaining different preferences for alternative transport providers’ categories. The implications of the results obtained are illustrated by simulating alternative policy scenarios. In conclusion, the paper underlines the need for rigorous policy analysis if the correct policy outcomes are to be estimated with an adequate level of accuracy.     

考虑出行群组异质性的有轨电车发展决策行为偏好研究

将有轨电车发展的研讨视为1项决策过程,按照基本属性、出行目的、出行模式等异质性特征,将出行群体划分为不同群组,开展基于不同异质性群组对发展有轨电车决策的偏好特性研究。通过融合行为偏好（RP）的意向偏好（SP）问卷调查,获取不同群组的基本属性及决策偏好特征数据。考虑有轨电车发展决策同时受环境要素、个体社会经济属性及出行需求特征等多层级因素的影响,对不同出行群组在不同情景下的决策偏好数据进行了多次测量,并引入多水平Logistic模型构建了考虑群组异质性的有轨电车发展决策偏好模型。选取了公共交通通勤出行、公共交通非通勤出行及非公共交通出行这3类异质性群组,对有轨电车发展决策偏好模型进行了参数估计。结果表明：①个体对有轨电车技术特性的感知并不会对有轨电车发展决策产生影响;②同一异质性群组,年龄的增长对有轨电车发展决策负向影响逐渐减小,说明年龄越大个体对有轨电车发展决策偏好逐渐增强,这种趋势在公共交通非通勤出行群组中更为显著;③可支配小汽车数对所有异质性群组的有轨电车发展决策偏好均呈显著负向影响,即家庭可支配小汽车数量越多,个体对有轨电车发展决策的支持度会越低;④出行时间及成本属性对有轨电车发展的决策偏好均有显著负向影响,即随着出行时间或出行成本的增加,对有轨电车发展决策的支持呈降低趋势,且该负向影响在骨架交通功能感知下较品质交通功能感知下更为敏感。     

融合手机信令数据的轨道交通出行分担率模型研究

交通行为模型广泛应用于城市出行需求分析等领域.传统行为模型的参数设置通常依赖经验判断,模型预测精度缺乏大样本验证手段.本文以重庆市解放碑-观音桥组团通道出行行为为研究对象,融合手机信令数据、AFC数据和问卷调查数据,构建随机参数分别为正态分布、均匀分布和γ分布的混合Logit模型,将手机信令数据与AFC数据分析结果作为...     

Incorporating refuelling behaviour and drivers’ preferences in the design of alternative fuels infrastructure in a city

The purpose of this article is to present an optimization model to plan the deployment strategy for hydrogen refuelling stations in a city when Origin–Destination (OD) data are not available. This model considers two objectives: to maximize the traffic covered by the selected hydrogen refuelling stations and minimize the average distance of the city’s inhabitants to the nearest hydrogen refuelling station. As OD data are assumed to be unavailable, the clustering of stations in the highest traffic zones is prevented by a new constraint that takes into account information on the distribution of existing conventional refuelling stations. This model is applied to Seville, a city in Southern Spain of about 140 km² with a population of around 700,000. This application uses the results of a survey of more than 200 Sevillian drivers on their current refuelling tendencies, their willingness to use alternative fuel vehicles and their minimum requirements (regarding maximum distance to be travelled to refuel and number of stations in the city) when establishing a network of alternative refuelling stations.     

Would personal carbon trading reduce travel emissions more effectively than a carbon tax?

The application of personal carbon trading (PCT) to transport choices has recently been considered in the literature as a means of reducing CO₂ emissions. Its potential effectiveness in changing car travel behavior is compared to the conventional carbon tax (CT) by means of a stated preferences survey conducted among French drivers (N ∼ 300). We show evidence that PCT could effectively change travel behavior and hence reduce transport emissions from personal travel. There is however a definite reluctance to reduce car travel. We were unable to demonstrate any significant difference between the effectiveness of PCT and the CT with regard to changing travel behavior. However, in the experiment, the PCT scheme provided consistent results while this was not the case for the CT scheme. Further research is needed into the “social norm” conveyed by a personal emissions allowance.     

On the semantic scale problem in stated preference rating experiments

Stated preference (SP) rating experiments are easy to design and conduct. Furthermore, they constitute a generalisation of SP choice experiments in terms of the information about preferences that can be achieved. However, its results are sensitive to the numerical values assigned by the analyst to the semantic scale used by individuals to express their preferences. We consider this problem in depth using a variety of statistical techniques, including ordinal probit and a novel optimal scale linear regression approach.