With the rapid adoption of electric micro-mobility vehicles (EMVs), the demand for efficient, user-centered battery-swapping infrastructure is rising. However, existing battery-swapping station (BSS) planning often falls short by neglecting critical elements such as user preferences and demand uncertainty. This study introduces a three-stage BSS planning framework that holistically addresses demand allocation, location-capacity optimization, and deployment adaptability under fluctuating demand. First, EMV users' preferences are integrated into a demand allocation model, capturing range anxiety and individual station selection criteria. This demand- sensitive allocation then informs a multi-objective bi-level planning model, balancing construction costs with user travel distances to BSS facilities. Finally, the model incorporates a demand uncertainty layer, supported by simulation scenarios, to create a robust facility deployment strategy that anticipates various demand fluctuations. An improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) effectively optimizes this model, validated through a case study in Nanjing, producing 41 Pareto-efficient solutions. Sensitivity analysis highlights how factors like range anxiety and charging time impact BSS planning outcomes, underscoring the value of this data-driven approach. This work demonstrates that a comprehensive planning approach, integrating user behavior and uncertainty considerations, can significantly enhance the effectiveness and adaptability of EMV battery-swapping networks.

Battery swapping services (BSS) offer innovative solutions to address the challenges of charging, safety, and battery management for electric micro-mobility vehicles (EMVs), such as electric bicycles and mopeds. However, the preferences and willingness to pay (WTP) for BSS among EMV users remain uncertain. This study examines consumer preferences for battery-swapping versus rechargeable EMVs within the Chinese market, identifying key factors that influence user choices. We employ a Stated Preference (SP) approach combined with a hybrid choice model (HCM) to assess both the economic viability and psychological impacts, including risk perception and social influence. Key findings demonstrate a higher willingness to pay for battery-swapping EMVs due to reduced charging time and enhanced safety features. Specifically, consumers are willing to pay an additional $2.86 for each minute reduction in facility accessibility time and an additional $27.52 for the safety features of battery-swapping EMVs. Existing users of traditional EMVs show a strong preference for BSS, willing to pay an additional $97.86. Frequent riders, who are more costsensitive, prefer rechargeable EMVs and are willing to reduce their expenditure by $47.95. In contrast, long-distance riders value battery endurance and are willing to pay an additional $55.96 for battery-swapping EMVs. These findings deepen our understanding of consumer behavior and provide valuable insights for policymakers and manufacturers aiming to optimize EMV adoption strategies.

Short-term prediction of on-street parking occupancy is essential to the ITS system, which can guide drivers in finding vacant parking spaces. And the spatial dependencies and exogenous dependencies need to be considered simultaneously, which makes short-term prediction of on-street parking occupancy challenging. Therefore, this paper proposes a deep learning model for predicting block-level parking occupancy. First, the importance of multiple points of interest (POI) in different buffers is sorted by Boruta, used for feature selection. The results show that different types of POI data should consider different buffer radii. Then based on the real on-street parking data, long short-term memory (LSTM) that can address the time dependencies is applied to predict the parking occupancy. The results demonstrate that LSTM considering POI data after Boruta selection (LSTM (+BORUTA)) outperforms other baseline methods, including LSTM, with an average testing MAPE of 11.78%. The selection process of POI data helps LSTM reduce training time and slightly improve the prediction performance, which indicates that complex correlations among the same type of POI data in different buffer zones will also affect the prediction accuracy of LSTM. When there are more restaurants on both sides of the street, the prediction performance of LSTM (+BORUTA) is significantly better than that of LSTM.

The battery swapping mode has the advantages of convenience and battery controllability, which can alleviate charging problems with electric micromobility vehicles (EMVs). The layout of battery swapping facilities and scheduling management can be carried out by accurately analyzing the high-resolution spatial-temporal distribution of battery swapping demand for EMV. This study establishes a prediction model framework for battery swapping demand of EMV using Monte Carlo simulation based on travel chains considering multi-source information interaction and behavior decision. Using real residential travel survey data of Nanning City, China and an empirical analysis with the city as a case study, the results show that the prediction framework proposed in this study is reliable. The temporal distribution of EMV battery swapping demand is closely related to the spatial distribution of travel. In addition, the demand characteristics of both centralized and decentralized battery swapping stations are evaluated separately. The peak intensity of the centralized mode is 18% greater than that of the decentralized mode when the battery swapping penetration is 35%. When the battery swapping penetration rate is low, decentralized mode can meet the peak swapping demand, and as the penetration rate increases, the effect of centralized mode is reflected. Finally, the total swapping path time considering multi-source information interaction and behavior decision is reduced by 7.8%. These findings allow for the study of battery swapping station planning and transportation planning.

The COVID-19 pandemic severely hampered the freedom of shopping travel while increasing individuals' interest in takeout. Although many studies have examined takeout shopping, the available literature provides insufficient evidence on the factors influencing takeout shopping demand under the COVID-19. In this study, generalized additive mixed models were developed based on sampling data of takeout orders in Nanjing before, during, and post the pandemic to measure the associations between takeout shopping demand and neighborhood characteristics at the business circle scale. The results show that population density, house prices, road density, and catering all have a significant impact on takeout shopping demand, while the roles of land use (residential and company indexes) before and post the pandemic are opposite. Besides, the factors influencing the recovery of the demand before and after the pandemic were analyzed. These findings provide important insights into the development of the takeout industry in the post-pandemic era.

To equilibrate the passenger distribution on the metro platform and carriage, a monetary incentive policy was explored in this paper; a discount on travel fare was provided to motivate metro passengers to queue for boarding in the noncrowded areas on the platform. The congested state is evaluated combined with the passenger distribution in the upcoming metro carriage. The utility of metro passengers and companies caused by the monetary incentive policy was analyzed, and the binary logit model was used to relate the utility to the passenger's willingness to move from crowded areas to noncrowded ones. With data acquired from the questionnaire survey, a regression analysis was employed to explain the variation in passengers' willingness to move as a function of discount level as well as personal and trip characteristics. The regression results show that effect of incentive discount is greater on female passengers and elderly passengers. A 10% discount can motivate most passengers aged over 40, and a 30% discount works on most female passengers. According to the different levels of passenger sensitivity, a particular discount can be determined to motivate a specific proportion of passengers to move and achieve the regulation of passenger distribution on the metro station platform and metro carriage.

为满足无人驾驶车辆快速获取路表构造的需求,建立了基于双侧窄角域摄影的沥青路表三维重构方法.对室内压实混合料和室外路面进行表面三维重构,将平均构造深度计算值与铺砂法、环绕摄影的构造深度值进行比较,验证所建重构方法的正确性.建立了构造标准差、单位面积上峰数、峰高标准差等构造评价新指标,并应用于AC-13、SMA-13和OGFC-13构造分析中.结果表明:双侧窄角域摄影重构方法能快速获取路表的三维点云,重建三维构造;基于该方法的构造深度计算值与铺砂法实测、环绕摄影重构的构造深度值相当,适用于室内压实混合料和室外实际路面;所建立的构造评价指标能从构造分布特征、胎/路接触应力集中度等角度为无人驾驶车辆提供完整的路表构造信息.

The red-light running (RLR) behaviors of urban mixed e-bike groups (delivery and ordinary e-bike) have become the main cause of traffic accidents at signalized intersections. The primary purpose of this study is to identify influencing factors of e-bike riders’ RLR behaviors, focusing on the role of delivery e-bike riders in mixed e-bike rider groups. Crossing behaviors of 4,180 e-bike samples (2006 delivery e-bikes and 2174 ordinary e-bikes) at signalized intersections are observed in Xi’an, China. The random parameter multinomial logit model is employed to capture the unobserved heterogeneous effects, and the effects of interaction terms are also considered. The results indicate that delivery e-bike riders are more likely to run red lights than ordinary e-bike riders. E-bike type, riders’ age, waiting position, traffic volume, traffic light type, and time of day are associated with crossing behaviors in urban mixed e-bike groups. In addition, the variable of traffic light status is found to account for unobserved heterogeneity. Findings are indicative to the development of effective implications in improving e-bikes’ traffic safety level at signalized intersections.

In order to solve the oversupply and repositioning problems of bike-sharing, this paper proposes an optimization model to obtain a reasonable supply volume scheme for bike-sharing and infrastructure configuration planning. The optimization model is constrained by the demand for bike-sharing, urban traffic carrying capacity (road network and parking facilities carrying capacities), and the flow conservation of shared bikes in each traffic analysis zone. The model was formulated through mixed-integer programming with the aim of minimizing the total costs for users and bike-sharing enterprises (including the travel cost of users, production and maintenance costs of shared bikes, and repositioning costs). CPLEX was used to obtain the optimal solution for the model. Then, the optimization model was applied to 183 traffic analysis zones in Nanjing, China. The results showed that not only were user demands met, but the load ratios of the road network and parking facilities with respect to bike-sharing in each traffic zone were all decreased to lower than 1.0 after the optimization, which established the rationality and effectiveness of the optimization results.

The performance of porous asphalt concrete is highly sensitive to its internal temperature. During summer, the high temperature of the pavement could cause permanent deformation and subsequently leads to serious clogging issues for porous asphalt concrete. This study investigated the feasibility of using a phase change composite as an aggregate replacement to regulate porous asphalt concrete temperature. The mass stability of four commonly used phase change materials including myristic acid, stearic acid, palmityl alcohol, and polyethylene glycol (PEG) was evaluated using thermogravimetric analysis. The PEG was selected, and its chemical and mass stability subjected to the constant high temperature, high temperature cyclic conditioning, and low temperature cyclic conditioning was further examined using Fourier transform infrared spectroscopy and differential scanning calorimetry. A phase change composite material using SiO2 as the shell and the PEG as the phase change material was fabricated and then mixed into porous asphalt concrete to replace the fine aggregate. The effect of the size and dosage of the PEG/SiO2 phase change composite on the internal temperature of porous asphalt concrete was experimentally investigated. It is found that the PEG/SiO2 composite with a 70% PEG mass content could be a good candidate for the porous asphalt concrete application. The appropriate phase change composite particle size range was determined as 0.6-1.18 mm, and the optimum replacement level was 1.4% by the total weight of aggregate. (C) 2020 Elsevier Ltd. All rights reserved.

为研究单粒径多孔隙聚氨酯碎石混合料(PPM)应用于路面铺装的可行性,通过飞散试验和析漏试验,确定了PPM中聚氨酯结合料的最佳用量,分析了PPM的高温稳定性、水稳定性、低温抗裂性、抗冻融性和耐久性等路用性能,并与多孔沥青混合料OGFC(开级配抗滑表层)进行了性能对比分析.结果表明:聚氨酯结合料的最佳用量为4.5％～6.8％;PPM的抗高温车辙能力远强于OGFC;PPM的最大弯拉应变满足改性沥青混合料的技术要求;不同条件下的PPM飞散损失值在较低水平,且其飞散损失程度低于OGFC;与OGFC相比,PPM具有更好的抗土粒堵塞能力.

Open-graded friction course (OGFC) is prone to deformation-related clogging during summer owing to high internal temperature. Reducing OGFC temperature during summer has significant benefits in relation to mitigation of rutting and clogging problems. A phase change composite material with PEG-4000 serving as the core and SiO2 as the shell (PEG/SiO2 ) was used to modify OGFC. Different mechanical performance tests were conducted, and the results showed that the addition of the PEG/SiO2 only has slight negative impacts. The effectiveness of the PEG/SiO2 modified OGFC for pavement temperature regulation was subsequently demonstrated through a lab-scale, indoor heating test using slab specimens with different PEG/SiO2 dosages and moisture conditions. Finally, a 3-D heat transfer modeling technique was applied to model the temperature field of the slab during the heating test. The results from the numerical simulation were generally in close agreement with the lab test results. Based on this study, using PEG/SiO2 phase change composite to modify OGFC can be considered as an effective approach to reducing porous pavement temperature during summer. (C) 2020 Elsevier Ltd. All rights reserved.