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With the rapid aging population, the number of elderly passengers using bus services for daily travel will increase. Thus, it is increasingly more important for bus companies to understand the specific service requirements and expectations of elderly passengers. However, we are not aware of any dedicated studies on service requirement and travel satisfaction of the elderly. As an inclusive society is considered a key aspect of sustainable development, guaranteeing travel satisfaction is a sine qua non for sustainable transportation. Therefore, this study develops a customer satisfaction index (CSI) for the elderly. Assuming that perceived service quality affects passenger satisfaction, a structural equation model (SEM) is estimated to derive the importance of 10 dimensions of perceived service quality: reliability, time schedule, route characteristics, ticketing, driver service, convenience, safety and security, cleanliness, comfort, and information services. Furthermore, the effect of satisfaction on loyalty and filing complaints against the service company is examined. SEM is developed using data from a bus passenger survey, administered in 2017 in Harbin, China. The results show while some service dimensions such as time schedule and reliability are less important to the elderly (possibly due to their less strict time constraints) other dimensions such as service and security, convenience and driver service are in need of improvement due to the fact that they are very important for the elderly but their perceived performance from the elderly’s perspective is low.

Abstract Plug-in hybrid electric buses (PHEBs) have the potential to satisfy both the fuel efficiency and the driving-mileage under complex urban traffic conditions. However, the optimal charge and discharge management is still a pivotal challenge of energy management for the inherent uncertainty in driving conditions. The common reference state-of-charge (SOC) profile based methods are limited by the adaptiveness which restricts the economic performance of on-line energy management systems. Promisingly, reinforcement learning based energy management strategies exhibited the significant self-learning ability. However, for PHEBs, the sparse rewards by the long-term SOC shortage make the strategies easily trick into the local optimal solution. The work presented in this paper concentrates on combining battery power reduction in the form of conditional entropy into reinforcement learning based energy management strategy. The proposed method named adaptive policy optimization (APO) introduces a novel advantage function to evaluate energy-saving performance considering long-term SOC dynamic, and a Bayesian neural network based SOC shortage probability estimator is utilized to optimize the energy management strategy parameterized by a deep neural network. Several experiments in a standard driving cycle demonstrate the optimality, self-learning ability and convergence of the APO. Moreover, the adaptability and robust performance get validated over the real bus trajectories data. With the comprehensive experiments in this paper, the proposed model exhibits enhanced fuel economy and more suitable SOC planning in comparison with the existing energy management strategies. The results indicate that APO respectively outperforms the compared online strategies by 9.8 % and 2.6 % and reaches 98 % energy-saving rate of the offline global optimum.

With the impacts of climate disruption becoming more evident there has been an increase in the uptake of climate change adaptation "toolkits" to assist local governments build community resilience and adapt to the impacts of climate change. There is increasing attention and call for practitioners to adopt proactive and participatory approaches to help in the adaptive response planning process. One such toolkit is the International Council for Local Environmental Initiatives (ICLEI) Asian Cities Climate Change Resilience Network (ACCRN) Process (IAP). This is a simple but rigorous toolkit developed to help local governments in Asian cities build resilience to the impacts of climate change. This paper outlines the application of the toolkit to determine its versatility in the rural context and was trialled in the Himalayan rural enclave of Ramgad in the Indian state of Uttarakhand. Given the differences between urban and rural environments, the outcomes highlighted the need for further investigation and analysis into the process to ensure that the methodology truly reflects the nature of rural systems and their level of vulnerability and adaptive capacity. Overall, the toolkit proved to be a simple but versatile toolkit to assess the vulnerability and adaptive capacity of communities in rural Himalaya. Over 40 resilience intervention strategies were developed for the Ramgad enclave and these were prioritized according to their technical, political, social and economic feasibility.

The global issue of climate change has accelerated the international commitment to net-zero carbon emission development. Decarbonizing the building sector has been put on several governments’ sustainable development agendas. To provide a reference for decarbonizing the building sector, this paper summarizes the U.S. experience in zero-carbon buildings (ZCBs) from the aspects of policies, codes, and standards at the federal and local levels and those of professional societies. Based on the definition and boundaries of ZCBs, this paper introduces policies on building energy efficiency, electrification, on-site renewable energy deployment, and “buy clean”, illustrating highlights in building phases, energy systems, materials production, and fiscal incentives. The synergic efforts and coordination between federal and local levels and with professional societies are also introduced. Successful experiences in policy and standard implementation are summarized, including the systemic work of multilevel governance, clearly defined goals and stringent policies, constant upgrades of codes and standards, transparency in reporting and information sharing, and increased financial and investment opportunities. This paper provides concrete recommendations for developing zero-carbon building policies.

The paper aimed to develop width recommendations for separated bicycle lanes considering abreast riding and overtaking behaviors. We investigated eight segments of separated bicycle lanes in Nanjing with cameras, analyzed the major types of abreast riding and overtaking, and then explored the volume threshold for two-abreast riding as well as the suitable clearances in a comfortable overtaking, using a binomial logistic model for both. The main results and conclusions are as follows: (1) two-abreast riding and an electric bicycle passing a conventional bicycle were the main categories of abreast riding and overtaking, respectively. (2) The volume threshold at which two-abreast riding occurred was 1075 bicycles/h/m. (3) Distances of 0.48 m, 1.48 m, and 0.56 m were the suitable clearances for the distance from the center of the passed rider to the nearest curb, the distance center to the center of riders while overtaking, and the distance from the center of the passing rider to the nearest curb, respectively. (4) Below 1075 bicycles/h/m, a bicycle lane 2 m in width was acceptable; above that, 2.5 m was suggested as the minimum width of the bicycle lane.

Abstract A successful transformation from conventional to renewable energy can contribute to climate change mitigation and regional development. As the co-host city of the 2022 Winter Olympic Games and the only National Renewable Energy Demonstration Zone in China, Zhangjiakou urgently needs to transform its conventional energy-dependent industries. This paper establishes a Long-range Energy Alternatives Planning System (LEAP)-Zhang model to examine the environmental and socio-economic effects of renewable energy development in Zhangjiakou by projecting the energy consumption and associated greenhouse gas emissions by various sectors over the 2016–2050 period. Two scenarios are designed, i.e., the business as usual scenario (BAU) and the integrated scenario (INT) (including three sub-scenarios, i.e., renewable energy alternatives scenario (REA), industrial structure optimization scenario (ISO) and energy saving facility scenario (ESF)). Results indicate that under the INT scenario, Zhangjiakou's greenhouse gas emissions would peak in 2030 with 13.23% of lower energy consumption when compared with the BAU scenario. Compared with the conventional energy, renewable energy shows competitive advantages in terms of greenhouse gas reduction, employment opportunities and economic costs. The total number of employees would reach 0.15 million by 2050 under the INT scenario, 1.71 times more than that in the BAU scenario. Feasible low-carbon pathways and co-benefits strategies associated with industrial transformation and renewable energy substitution should be pursued in future actionable plans. Results found in this study could provide importance information for achieving renewable energy transformation and associated socio-environmental benefits within and beyond China.

The impact of trucks on road traffic safety has been extensively studied, but the factors influencing truck crash injury severity have not yet been examined from the quarterly perspective. Crash data for Shandong Province in China for 10 years (2012–2021) were reviewed to investigate the transferability of the determinants of the severity of truck crash injuries in four quarters. Three injury severity levels were considered and a random parameters logit model (RPL) considering the heterogeneity of means and variances was constructed to assess the factors affecting the severity of crash injury. The significant variables were explored from the influencing factors of driver, vehicle, crash type, road, environment, and temporal characteristics. A likelihood ratio test was employed to assess the transferability of the crash model over four quarters, and we used marginal effects to analyze the stability of the influencing factors. The results indicated that there was instability among the four quarterly variables that had to be modeled separately. There were also some variables, such as heavy vehicle and multiple-vehicle crashes, that simultaneously affected the severity of truck crash injuries across the four quarters, but the degree of impact was different. The results could enable engineers and policy makers to better formulate management rules and propose appropriate measures according to quarterly changes.

Road network traffic management and control are the key mechanisms to alleviate urban traffic congestion. With this study, we aimed to characterize the traffic flow state of urban road networks using the Macroscopic Fundamental Diagram (MFD) to support area traffic control. The core property of an MFD is that the network flow is maximized when network traffic stays at an optimal accumulation state. The property can be used to optimize the temporal and spatial distribution of traffic flow with applications such as gating control. MFD construction is the basis of these MFD-based applications. Although many studies have been conducted to construct MFDs, few studies are dedicated to improving the accuracy considering the reliability of different sources of data. To this end, we propose an MFD construction method using multi-source data based on Dempster–Shafer evidence (DS evidence) theory considering the reliability of different data sources. First, the MFD was constructed using VTD and CSD, separately. Then, the fused MFD was derived by quantifying the reliability of different sources of data for each MFD parameter based on DS evidence theory. The results under real data and simulated data show that the accuracy of the constructed MFDs was greatly improved considering the reliability of different data sources (the maximum MFD estimation error was reduced by 22.3%). The proposed method has the potential to support the evaluation of traffic operations and the optimization of signal control schemes for urban traffic networks.