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.     

扰动下的生物质能供应链收益共享契约研究

以协调扰动下的生物质能供应链为目的,在"公司+基地+农户"组织模式的基础上,分别研究供应链在无扰动、供给扰动和需求扰动下的最优订购量、收益分配系数以及单位收购价的变化,从而协调扰动的生物质能供应链,实现系统利润最大化。     

Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach

This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored; GCNN_reg-DDGF is a regular GCNN-DDGF model which contains the convolution and feedforward blocks, and GCNN_rec-DDGF additionally contains a recurrent block from the Long Short-term Memory neural network architecture to capture temporal dependencies in the bike-sharing demand series. Furthermore, four types of GCNN models are proposed whose adjacency matrices are based on various bike-sharing system data, including Spatial Distance matrix (SD), Demand matrix (DE), Average Trip Duration matrix (ATD), and Demand Correlation matrix (DC). These six types of GCNN models and seven other benchmark models are built and compared on a Citi Bike dataset from New York City which includes 272 stations and over 28 million transactions from 2013 to 2016. Results show that the GCNN_rec-DDGF performs the best in terms of the Root Mean Square Error, the Mean Absolute Error and the coefficient of determination (R²), followed by the GCNN_reg-DDGF. They outperform the other models. Through a more detailed graph network analysis based on the learned DDGF, insights are obtained on the “black box” of the GCNN-DDGF model. It is found to capture some information similar to details embedded in the SD, DE and DC matrices. More importantly, it also uncovers hidden heterogeneous pairwise correlations between stations that are not revealed by any of those matrices.     

A dynamic station strategy to improve the performance of flex-route transit services

As an innovative combination of conventional fixed-route transit and demand responsive service, flex-route transit is currently the most popular type of flexible transit services. This paper proposes a dynamic station strategy to improve the performance of flex-route transit in operating environments with uncertain travel demand. In this strategy, accepted curb-to-curb stops are labeled as temporary stations, which can be utilized by rejected requests for their pick-up and drop-off. The user cost function is defined as the performance measure of transit systems. Analytical models and simulations are constructed to test the feasibility of implementing the dynamic station strategy in flex-route transit services. The study over a real-life flex-route service indicates that the proposed dynamic station strategy could reduce the user cost by up to 30% without any additional operating cost, when an unexpectedly high travel demand surpasses the designed service capacity of deviation services.     

Taxi vacancy duration: a regression analysis

Taxi vacancy duration is a major efficiency measure for taxi services. A clear understanding of the various factors and their effect on vacancy duration is necessary for the optimal operational management of taxis. Previous research has only dealt with vacancy duration by assuming probability distributions and has not investigated heterogeneity in the data caused by various factors. We develop a parametric duration model using not only new operational characteristics but also variables associated with taxi demand, such as weather, land use, demographics, socioeconomic variables, and accessibility of public transportation. The model is applied to a large-scale New York City (NYC) taxi trip dataset that covers operations for 2013. The results show that all the attributes have significant associations with vacancy duration that follows a log-normal distribution. Our study is expected to help improve the efficiency of taxi operations by decreasing the time spent in vacant states.