基于TDM的城市交通拥挤经济分析及对策

针对城市交通拥挤状况,就城市交通供给与需求进行简要分析,提出解决城市交通拥挤问题应依赖于TDM,并从经济角度详细分析城市交通拥挤问题;从城市规划、城市交通结构、出行成本等方面提出解决城市交通拥挤的对策。通过研究,得出解决城市交通拥挤问题的主要途径,TDM在解决我国城市交通拥挤问题方面有重要的作用。     

城市路内停车宏观比例优化模型

科学合理地规划路内停车,不仅要求规划工作步骤科学,而且,宏观上要求路内停车占整个公共停车比例保持在合理的范围之内.文章分别分析了路内停车需求影响因素和路内停车供给约束条件,并把这些因素进行了量化.通过综合考虑影响路内停车需求的各种因素和路内停车供应的各种约束条件,在供需平衡的约束条件下建立非线性优化模型,并对其算法进行了分析探讨.最后将模型应用到铜陵市实际项目规划中.结果表明,该模型在应用中具有较高的实用性.     

Commuters’ acceptance of and behavior reactions to license plate restriction policy: A case study of Tianjin,China

License plate restriction (LPR) policies are currently being implemented in major Chinese cities with the aim of mitigating traffic congestions. Meanwhile, much controversy regarding the effectiveness of the LPR policies is arising. To better understand the impact of the LPR policies, this paper studies commuters’ acceptance of and behavior reactions to the policies after their implementation. A theoretical model was proposed as the first step, followed by a questionnaire survey that was conducted in Tianjin, China, where an LPR policy has been in place since March 2014. Car owners frequently commuting within the restricted area were sampled as respondents, and a multi-variable regression method was employed to analyze the collected survey data. The results indicate that it is necessary to promote public’s acceptance of the LPR policy, because lower acceptance will lead to more negative reactions towards the policy, which may weaken its effectiveness. Main factors affecting the level of acceptance of the policy are also found, which may serve as a reference for transportation authorities seeking to increase commuters’ acceptance of the policy. These findings are beneficial to designing and implementing LPR policies.     

Explaining “peak car” with economic variables

Many western countries have seen a plateau and subsequent decrease of car travel during the 21st century. What has generated particular interest and debate is the statement that the development cannot be explained by changes in traditional explanatory factors such as GDP and fuel prices. Instead, it has been argued, the observed trends are indications of substantial changes in lifestyles, preferences and attitudes to car travel; what we are experiencing is not just a temporary plateau, but a true “peak car”. However, this study shows that the traditional variables GDP and fuel price are in fact sufficient to explain the observed trends in car traffic in all the countries included in our study: the United States, France, the United Kingdom, Sweden and (to a large extent) Australia and Germany. We argue that the importance of the fuel price increases in the early 2000s has been underappreciated in the studies that shaped the later debate. Results also indicate that GDP elasticities tend to decrease with rising GDP, and that fuel price elasticities tend to increase at high price levels and during periods of rapid price increases.     

Predicting short-term bus passenger demand using a pattern hybrid approach

This paper proposes an Interactive Multiple Model-based Pattern Hybrid (IMMPH) approach to predict short-term passenger demand. The approach maximizes the effective information content by assembling the knowledge from pattern models using historical data and optimizing the interaction between them using real-time observations. It can dynamically estimate the priori pattern models combination in advance for the next time interval. The source demand data were collected by Smart Card system along one bus service route over one year. After correlation analysis, three temporal relevant pattern time series are generated, namely, the weekly, daily and hourly pattern time series. Then statistical pattern models are developed to capture different time series patterns. Finally, an amended IMM algorithm is applied to dynamically combine the pattern models estimations to output the final demand prediction. The proposed IMMPH model is validated by comparing with statistical methods and an artificial neural network based hybrid model. The results suggest that the IMMPH model provides a better forecast performance than its alternatives, including prediction accuracy, robustness, explanatory power and model complexity. The proposed approach can be potentially extended to other short-term time series forecast applications as well, such as traffic flow forecast.     

The concept and impact analysis of a flexible mobility on demand system

This paper introduces an innovative transportation concept called Flexible Mobility on Demand (FMOD), which provides personalized services to passengers. FMOD is a demand responsive system in which a list of travel options is provided in real-time to each passenger request. The system provides passengers with flexibility to choose from a menu that is optimized in an assortment optimization framework. For operators, there is flexibility in terms of vehicle allocation to different service types: taxi, shared-taxi and mini-bus. The allocation of the available fleet to these three services is carried out dynamically so that vehicles can change roles during the day. The FMOD system is built based on a choice model and consumer surplus is taken into account in order to improve passenger satisfaction. Furthermore, profits of the operators are expected to increase since the system adapts to changing demand patterns. In this paper, we introduce the concept of FMOD and present preliminary simulation results. It is shown that the dynamic allocation of the vehicles to different services provides significant benefits over static allocation. Furthermore, it is observed that the trade-off between consumer surplus and operator’s profit is critical. The optimization model is adapted in order to take into account this trade-off by controlling the level of passenger satisfaction. It is shown that with such control mechanisms FMOD provides improved results in terms of both profit and consumer surplus.     

道路交通系统演化的非线性动力模型

为揭示各类交通规划管理政策和先进技术对道路交通系统的影响，构建了描述能源储备 量、机动车保有量和路网面积动态演化的三维非线性动力模型，重点对均衡解的存在性、唯一性 和稳定性进行分析。同时，通过数值试验验证了所得结论的正确性。结果表明：若所有变量及参 数为正，则有实际意义的均衡解唯一且稳定；若满足可供机动车行驶的能源增长率降低城镇开发 边界扩大、共享泊位供给量增加、机动车的平均能源消耗量增加中任一条件，则均衡平均车速提 高；当满足一定条件时，路网面积和路网容量的换算系数增加会提高均衡平均车速；存在最优行 驶机动车比例，可使均衡路网饱和度达到最低。     

时间社会学视角下老年人对家庭出行行为的影响研究

为阐述家庭微观社会情境中老年人与同住成员之间的行为交互对个体出行决策的影响，本文从时间社会学视角出发，以昆明市有老年人家庭为分析单元，用家庭贡献度量化老年人家务活动的分担程度，构建结构方程模型揭示老年人对家庭成员日常活动时间结构的影响，进而剖析成员间的活动交互影响机理.结果表明：老年人家庭贡献度增加时，成员间在非通勤活动上的交互效用减弱；由于女家长在时间结构上呈现隐形的“时间贫困”特征，其在活动—出行模式上受老年人家庭贡献度的影响显著高于男家长；与此同时，老年人作用下女家长在非通勤活动出行决策时优先考虑家务活动的时间安排，而男家长则优先考虑自由活动的安排.     

Regional climate impact of aerosols emitted by transportation modes and potential effects of policies on demand and emissions

The transportation system is one of the main sectors with significant climate impact. In the U.S. it is the second main emitter of carbon dioxide. Its impact in terms of emission of carbon dioxide is well recognized. But a number of aerosol species have a non-negligible impact. The radiative forcing due to these species needs to be quantified. A radiative transfer code is used. Remote sensing data is retrieved to characterize different regions. The radiative forcing efficiency for black carbon are 396 ± 200 W/m²/AOD for the ground mode and 531 ± 190 W/m²/AOD for the air transportation, under clear sky conditions. The radiative forcing due to contrail is 0.14 ± 0.06 W/m² per percent coverage. Based on the forcing from the different species emitted by each mode of transportation, policies may be envisioned. These policies may affect demand and emissions of different modes of transportation. Demand and fleet models are used to quantify these interdependencies. Depending on the fuel price of each mode, mode shifts and overall demand reduction occur, and more fuel efficient vehicles are introduced in the fleet at a faster rate. With the introduction of more fuel efficient vehicles, the effect of fuel price on demand is attenuated. An increase in fuel price of 50 cents per gallon, scaled based on the radiative forcing of each mode, results in up to 5% reduction in emissions and 6% reduction in radiative forcing. With technologies, significant reduction in climate impact may be achieved.     

A joint optimization model for liner container cargo assignment problem using state-augmented shipping network framework

This paper proposes a state-augmented shipping (SAS) network framework to integrate various activities in liner container shipping chain, including container loading/unloading, transshipment, dwelling at visited ports, in-transit waiting and in-sea transport process. Based on the SAS network framework, we develop a chance-constrained optimization model for a joint cargo assignment problem. The model attempts to maximize the carrier’s profit by simultaneously determining optimal ship fleet capacity setting, ship route schedules and cargo allocation scheme. With a few disparities from previous studies, we take into account two differentiated container demands: deterministic contracted basis demand received from large manufacturers and uncertain spot demand collected from the spot market. The economies of scale of ship size are incorporated to examine the scaling effect of ship capacity setting in the cargo assignment problem. Meanwhile, the schedule coordination strategy is introduced to measure the in-transit waiting time and resultant storage cost. Through two numerical studies, it is demonstrated that the proposed chance-constrained joint optimization model can characterize the impact of carrier’s risk preference on decisions of the container cargo assignment. Moreover, considering the scaling effect of large ships can alleviate the concern of cargo overload rejection and consequently help carriers make more promising ship deployment schemes.