The need to develop new charging practices for electric micromobility vehicles (EMVs) is inevitable due to the challenges associated with charging and managing their batteries. Intelligent battery swapping services for EMVs have expanded from being business-to-business to business-to-customer. This battery swapping model offers the advantages of convenience and battery controllability, effectively addressing the current charging issues faced by EMVs. To assess the feasibility and potential success of promoting intelligent battery swapping services, this study conducts a stated choice experiment to investigate the charging behavior of EMV users across different modes, such as self-operated charging, charging at charging stations, and intelligent battery swapping. We propose a charging and swapping choice modeling approach that combines cumulative prospect theory and multi-attribute decision making methods to model charging decisions under uncertainty. The results demonstrate that our proposed method surpasses conventional models in terms of model goodness-of-fit and behavioral interpretation. Furthermore, heterogeneity was observed among EMV users in their charging and swapping behaviors. Based on these findings, we discuss policy implications for creating sustainable cities, with a particular focus on establishing intelligent battery swapping facilities.

With the rapid development of the food delivery industry, efficient battery-swapping services have become a critical factor in enhancing the delivery efficiency of delivery electric micro-mobility (DEM). However, issues such as battery-swapping queues and insufficient battery levels after battery-swapping significantly reduce drivers’ satisfaction with the service. To address the inefficiencies in DEM battery-swapping, this study integrates a discrete choice model of driver preferences with a multi-agent reinforcement learning (MARL) framework, forming an interactive decisionmaking system. This approach enables the optimization of battery-swapping timing and station selection while improving recommendation service satisfaction. The system employs a “centralized training, decentralized execution” approach with improved MAPPO (IMAPPO), where agents share global information during training but operate independently when executing, deciding swaps based on local states. Experiments show this IMAPPO significantly cuts rejected swap recommendations, reduces queue and detour times, and boosts order completion rates. It outperforms baseline algorithms across multiple metrics, adapting dynamically to optimize for both driver satisfaction and operational efficiency.

As electric micro-mobility vehicles (EMVs) such as e-bikes and e-scooters increasingly meet daily commuting and delivery needs, battery swapping services have become a convenient charging method. However, unordered battery swapping disrupts user experience and operational efficiency at battery swapping stations (BSSs). Additionally, the mismatch between battery-swapping demand and available batteries leads to idle batteries and resource waste. Therefore, it is crucial to provide organized battery-swapping recommendation services while also channeling stored energy from idle batteries back to the grid. We model the operation of EMVs and battery-swapping stations (BSSs) as a Partially Observable Markov Decision Process (POMDP), where each BSS acts as an agent interacting with the environment and other stations. We introduce a multi-agent hierarchical reinforcement learning model to address the asynchronous nature of swapping recommendations and charging-discharging actions. Our model aims to minimize both the total swapping time and the operational costs of BSSs. Through numerical studies using EMV battery-swapping demand data from Nanjing, we demonstrate the model's effectiveness in reducing detour and queuing times, improving swapping success rates, and balancing service loads. The proposed model and algorithm show strong generalization capabilities across different operational environments, indicating their potential for broader application in optimizing EMV BSS management.

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.

In the context of future-oriented urban road infrastructure adaptation, it is crucial to understand the factors that influence cyclists’ route choice behavior to promote cycling effectively. However, the increase in bicycle usage can also lead to crowded cycling infrastructure and increased objective safety risks, which has not been thoroughly investigated in previous studies. This study examine how crowding, safety risks, and route attributes influence cyclists’ route choices, differentiating between e-bike and regular bike users. Using stated preference data from 784 cyclists in Nanjing and a hybrid choice model (HCM), the research integrates latent attitudinal variables and socio-demographic factors. Findings show regular cyclists are more sensitive to crowding and accident risk, while e-bike users prioritize dedicated cycling infrastructure over travel time and road characteristics. The results underscore the importance of including attitudinal variables in route choice models and provide valuable guidance to safely manage rising cycling demand and improve navigation systems.

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.

Battery-swapping service for electric micro-mobility vehicles (EMVs) provides users with a convenient way to replenish energy to extend travel range. However, massive integration of battery-swapping EMVs potentially causes disorderly swaps and negatively impacts user experience and operational efficiency. Considering shared battery-swapping stations (BSSs) for both ordinary and delivery EMVs, this study proposes a real-time BSS recommendation system based on deep reinforcement learning to enhance the quality of battery-swapping service. A reservation-based recommendation mode is designed to ensure a first-reserved, first-served swapping service, for which the long short-term memory (LSTM) and dynamic graph attention networks (DGAT) modules are developed to capture the dynamic correlation between EMVs and BSSs for predicting and representing upcoming swapping demands to avoid unintended competition for swapping resources. This recommendation system incorporates a utility function for swapping choices into the traditional system optimization-based reward function to ensure optimal system performance while enabling personalized recommendations. An improved Rainbow algorithm is suggested to enhance the performance of the battery-swapping recommendation strategy by integrating techniques such as prioritized experience replay and double Q-learning. Extensive numerical experiments demonstrate that the proposed LDRFusion algorithm outperforms several baseline methods.

Intelligent Battery Swapping Services (BSSs) present an innovative solution to the challenges of charging, safety hazards, and disorganized battery management encountered by electric micromobility vehicles (EMVs). Although BSSs have gained traction in the business-to-business domain, their acceptance in the business-to-customer sector remains uncertain. This study leverages the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) combined with a structural equation modeling framework to discern the adoption intentions toward BSS among individual drivers. Drawing from a survey of 434 EMV users in Jiangsu Province, China, we analyzed the relationship between latent variables, while also evaluating the moderating effects of sociodemographic and transport-related factors via multi-group analysis. The findings revealed a model with impressive explanatory power, accounting for approximately 54.8% of the variance in intention. Notably, social influence emerged as the most potent influencer on intention, trailed by effortlessness expectancy, price sensitivity, and performance expectancy. Intriguingly, both price sensitivity and technology anxiety exhibited a negative correlation with intention. Furthermore, variables such as gender, age, income, riding purpose, and riding frequency were found to significantly shape users’ intentions to embrace BSS. This research offers valuable insights for policy makers aiming to promote BSS adoption among EMV users and encourage the EMV market’s growth and sustainability.

As the delivery sector increasingly relies on electric micromobility vehicles (EMVs), the urgency for efficient battery-swapping infrastructure becomes critical. This study develops a comprehensive framework for predicting battery-swapping demand for delivery EMVs (DEMVs) based on an activity-based travel chain simulation model and devises a multi-objective optimization model for the strategic placement of battery-swapping stations. The simulation model integrates submodules such as the EMV generation and attraction model, Agent-based EMV travel chain, and EMV and battery-swapping behavior model to capture the nuanced travel patterns and battery-swapping demand. Leveraging the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the study optimizes the network design of battery-swapping stations considering both construction and travel costs. A case study in Nanjing City, representative of the diverse delivery sector's operations, substantiates the simulation's accuracy, maps out the spatiotemporal distribution of swapping demand, and analyzes the Pareto optimal set derived from the optimization model. Sensitivity analysis focuses on the facility planning model, assessing how uncertainties in swapping demand and battery charging rates within stations impact operational efficacy. This research melds demand forecasting with infrastructure optimization, providing actionable insights for the planning and management of battery-swapping stations.

The integration of public bicycles with tube systems has the potential to enhance urban transportation sus-tainability and efficiency. However, the utilization of public bicycles can significantly differ among various docking stations, even when they fall within the coverage area of the same tube station. Understanding the factors that attract or discourage individuals from using public bicycles in different locations is essential for informed decision-making regarding infrastructure and policy enhancements. This study examines the disparity in public bicycle usage, with a specific focus on the micro-level walking environments along connecting paths between docking stations and tube stations. Transaction data collected from London public bicycle and tube systems are utilized, alongside micro-level walking environment and macro-level built environment and socio-economic factors. A generalized additive mixed model is employed to capture nonlinear effects and spatial au-tocorrelations in docking station usage.The results reveal significant effects of walking environments along connecting paths on docking station usage. Specifically, positive associations are observed between bicycle station usage and footpaths quality, presence of pedestrian crossing, and points of interests (e.g., green space) along the connecting path, as well as tube ridership, cycle facilities, and land-use mixture. Conversely, negative relationships are observed with walking distance, physical barriers, road crossed, and bus stops. Moreover, proximity to transit, distance to the city center, and deprivation level demonstrate threshold effects on bicycle station usage. This study provides several practical implications for improving the multi-mode transportation system and the planning of docking stations around tube station, aiming to create cycling-friendly and transit-oriented environments.

Bike-sharing offers a convenient transportation option, enhancing the potential for direct competition with underground transportation, especially for short-distance trips. However, research on bike-sharing trips primarily focuses on survey data or aggregated data at the station-level. Few attempts have been made to under -stand the competition between bike-sharing and underground at the origin-destination (OD) level. This study aims to explore the competitiveness of bike-sharing to the underground at short-distance level using actual OD-level bike-sharing and underground ridership data collected in London. Light Gradient Boosting Machine and SHapley additive explanations models are employed for the analysis.Our results found that bike-sharing can serve as a competitor to the underground, especially in denser urban areas and peak periods. The competitiveness of bike-sharing is associated with the attributes of trips' origins and destinations, route characteristics, and time. In particular, the route characteristics of travel duration/distance, road gradient, bike infrastructure availability and the number of crossings are correlated with the competitiveness of bike-sharing to the underground. Moreover, it is found that users pay more attention to the characteristics of origins rather than destinations. Our findings can provide valuable implications for promoting bike-sharing as a substitution to underground service.

Intelligent battery-swapping services for electric micromobility vehicles (EMVs) are expanding from To-business to To-customer sides, offering consumers greater convenience while addressing charging challenges. This study proposes an activity-based travel chain simulation framework to predict battery-swapping demand for ordinary and delivery EMVs. A case study in Nanjing City shows that temporal distribution of EMV battery-swapping demand is related to travel patterns, while spatial distribution is correlated with EMV generation and traffic zones. Sensitivity analysis examines the impact of swapping penetration, initial power, and swapping threshold on the swapping demand. Findings suggest that increasing swapping penetration by 1% leads to a rise of 692 and 139 in one-day swapping demand for ordinary and delivery EMVs, respectively. Furthermore, higher initial power leads to lower one-day swapping demand, while increasing the swapping threshold leads to higher swapping demand. These findings offer important insights for battery-swapping station planning and operation management.

With the dramatic increase in the number of cyclists, cycling safety has become a critical issue worldwide. It is important to ensure a safe, comfortable, and continuous cycling environment for cyclists. However, the safety effects of discontinuities in cycle network have been greatly ignored in the literature. This study aims to investigate the safety effects of discontinuities in cycle network inside and between Traffic Analysis Zones (TAZs) in London. Bayesian hierarchical spatial model is employed using the number of accesses as spatial weights. Covariates, including cycle network characteristics, road network characteristics, exposure variables, cycle-related facilities, traffic characteristics, and environmental conditions are also considered in the model. The results reveal that the discontinuities in cycle network are significantly associated with cycle crashes. For inside-TAZ level, "discontinuity in cycle lane class" and "standard deviation of cycle link length" have positive effects on cycle crashes. For between-TAZs level, the discontinuities in cycle lane accesses, and cycle lanes and tracks density are positively associated with cycle crashes. The results emphasize the importance of minimizing the discontinuities in cycle network, not only inside the TAZ but also between TAZs. This study also provides several practical implications for future cycling infrastructure planning and construction.

Cycling has gained increasing popularity worldwide as a healthy and sustainable mode of travel. This study aims to investigate the factors affecting the cycle count at the road segment level based on a detailed survey dataset of 794 road segments in London from 2015 to 2019. A spatial regression model was employed to control for possible spatial correlations among neighboring count points. The influencing factors at three spatial levels were considered in this study, including the characteristics of the target road segment, adjacent road segments, and adjacent areas. In addition, we investigated the differences in cycle counts between private and rental cycles during different time periods (morning peak, evening peak, and off-peak hours). The results indicated that private cycle count was positively correlated with cycle facility, public transit, minor road, network continuity, and connectivity. A similar positive effect was found for rental cycles, although the magnitude of this effect was smaller. In addition, parking facilities (i.e., docking stations and cycle parking) had significant impacts on cycle counts for both private and rental cyclists. This study provides several practical suggestions for improving cycling environments.

Introduction: With a significant increase in accidents involving cyclists, more attention has been paid to cycling safety. Previous studies on traffic accident revealed that red-light violations of non-motorized vehicles have become the leading cause of crashes at signalized intersections. The objective of this study is to investigate the impact of non-motorized traffic enforcement cameras (NTECs) on the red-light run-ning behavior of cyclists, including ordinary e-bike riders, delivery e-bike riders, and bicyclists. Method: An observational study of 5,217 cyclists was conducted at six primary intersections in the downtown areas of Nanjing, China. A random parameter logit model was used to explore the safety effect of the NTECs and other factors related to red-light violation behavior. Results: The results indicate higher reduc-tions in red-light violations at intersections with the NTECs compared than at the non-adjacent intersec-tions without the NTECs. Furthermore, the NTECs demonstrated a beneficial but smaller impact on the reduction of violations at adjacent intersections. Another primary finding was that the effects of the NTECs varied among three types of cyclists (ordinary e-bike riders, delivery e-bike riders, and bicyclists). Conclusions: The NTECs were found to be most effective in the case of delivery e-bike riders, followed by ordinary e-bike riders and bicyclists. In addition, the factors associated with the red-light violation behaviors of these three groups were also found to be different. In general, group size, maximum waiting time, waiting position, and visual search were significantly related to the probability of red-light viola-tions in all three groups. Practical Applications: Based on these findings, this study provides some feasible suggestions for improving the effect of the NTECs and for the future extension of the NTECs installation, such as the randomization of the enforcement and publicity campaigns.(c) 2022 National Safety Council and Elsevier Ltd. All rights reserved.

As vulnerable traffic participants, electric bike (EB) riders have suffered from high collision casualties in recent years. Road user anger has been shown to affect riding behavior and lead to traffic accidents. Besides, studies have highlighted that there may be differences in road user anger in driving different vehicles due to varying perceptions of the relative vulnerability of vehicle type characteristics (control performance and cognitive processes). However, current road user anger investigations for two-wheelers have focused mainly on conventional cyclists, and little attention has been paid to e-bike riders, especially with the emerging group of delivery e-bike (DEB) riders. This study aims to develop a Cycling Anger Scale (CAS) for EB riders based on the Cycling Anger Scale and explore the road user anger experienced by EB riders and the differences between ordinary and delivery EB riders. The survey was conducted in Nanjing, China, and collected from 281 Ordinary EB (OEB) riders and 268 DEB riders. Exploratory factor analysis and confirmatory factor analysis are conducted to determine the revised four-factor structure of the 14-item CAS for EB. The results show that the scores of police interaction and cyclist interaction on the CAS subscales are significantly different between the OEB and DEB groups. The police interaction is the largest source of anger for both groups. Besides, the aggressive riding behaviors are significantly correlated with riding anger, which can be predicted by different aspects of riding anger for the two types of EB riders. This study provides a theoretical basis for designing intervention measures and safety education programs to enhance EB riders’ road safety.

Sustainable transport policies aim to promote the well-being of all transport participants. Although the travel satisfaction of ordinary travelers has received extensive attention in previous studies, few have focused on the important bearer groups of the last mile urban logistics, especially with the current influx of delivery electric two-wheeler (DETW) riders on the road. This study aims to quantitatively explore the impact of riding perception, delivery attributes, built environment, and demographic variables on delivery travel satisfaction of DETW riders. Data were collected in Nanjing, China, through a questionnaire survey. The results show that the inclusion of built environment variables has provided additional variance explanation and proved their direct or indirect effect on delivery travel satisfaction. Besides, safety and accessibility, environmental comfort, delivery convenience, policy acceptance, and perceived stress positively affect delivery travel satisfaction, while delivery punishment has a negative effect. Delivery convenience is the most important variable explaining delivery travel satisfaction. Finally, policies at all levels, including individual riders, platforms, consumers, and public administration, are proposed to enhance the DETW riders’ well-being and make the food delivery industry more sustainable and appealing.

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.

Characterizing the relationship between signal timing and particulate matter (PM) is important for a sustainable traffic signal control and accurate exposure assessment at emission hotspot locations. The impact of traffic signal timing and meteorological variables on size-resolved PM in the 0.25-10 mu m range at the signalized intersection was investigated. The PMs, particle number concentrations (PNCs), and particle mass concentrations (PMCs) in the given size distribution were obtained at the signalized intersection in Xi'an. A comprehensive analysis method involving statistical analysis, regression analysis, simulation was used. The results demonstrated that the fine particles (PMC0.25-2.5 and PNC0.25-2.5) have a positive relationship with wind speed and vehicles per cycle, and a size ranges of PM, ranging from 2.5 to 10 mu m, was greatly affected by those factors. Moreover, the regression findings via multivariate multiple linear regression (MMLR) showed that the coefficients of independent variables (temperature, and delay) were statistically significant, which indicated that those factors enormously affect fine particles. In addition, the simulation results from the CAL3QHC indicated the benefit of pollutant reduction (PM2.5 reduced by an average of 5.3%) becomes more significant as signal timing optimization. This study serves as a demonstration of the abilities of reasonable signal timing to improve air quality and make better at the service level of the intersection.

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.