Search

Mobility‐as‐a‐Service (MaaS) is an emerging business model integrating various travel modes into a single mobility service accessible on demand. Besides the on‐demand mobility services, instant delivery services have increased rapidly and particularly boomed during the coronavirus (COVID‐19) pandemic, requiring online orders to be delivered timely. In this study, to deal with the redundant mobility resources and high costs of instant delivery services, we model an MaaS ecosystem that provides mobility and instant delivery services by sharing the same multimodal transport system. We derive a two‐class bundle choice user equilibrium (BUE) for mobility and delivery users in the MaaS ecosystems. We propose a bilateral surcharge–reward scheme (BSRS) to manage the integrated mobility and delivery demand in different incentive scenarios. We further formulate a bilevel programming problem to optimize the proposed BSRS, where the upper level problem aims to minimize the total system equilibrium costs of mobility and delivery users, and the lower level problem is the derived two‐class BUE with BSRS. We analyze the optimal operational strategies of the BSRS and develop a solution algorithm for the proposed bilevel programming problem based on the system performance under BSRS. Numerical studies conducted with real‐world data validate the theoretical analysis, highlight the computational efficiency of the proposed algorithm, and indicate the benefits of the BSRS in managing the integrated mobility and delivery demand and reducing total system equilibrium costs of the MaaS ecosystems. [ABSTRACT FROM AUTHOR]

Path planning for the multiagent, which is generally based on the artificial potential energy field, reflects the decision-making process of pedestrian walking and has great importance on the field multiagent system. In this paper, after setting the spatialtemporal simulation environment with large cells and small time segments based on the disaggregation decision theory of the multiagent, we establish a generalized dynamic potential energy model (DPEM) for the multiagent through four steps: (1) construct the space energy field with the improved Dijkstra algorithm, and obtain the fitting functions to reflect the relationship between speed decline rate and space occupancy of the agent through empirical cross experiments. (2) Construct the delay potential energy field based on the judgement and psychological changes of the multiagent in the situations where the other pedestrians have occupied the bottleneck cell. (3) Construct the waiting potential energy field based on the characteristics of the multiagent, such as dissipation and enhancement. (4) Obtain the generalized dynamic potential energy field by superposing the space potential energy field, delay potential energy field, and waiting potential energy field all together. Moreover, a case study is conducted to verify the feasibility and effectiveness of the dynamic potential energy model. The results also indicate that each agent's path planning decision such as forward, waiting, and detour in the multiagent system is related to their individual characters and environmental factors. Overall, this study could help improve the efficiency of pedestrian traffic, optimize the walking space, and improve the performance of pedestrians in the multiagent system., 1. Introduction Multiagent system used in the simulation reflects the psychological and physical properties of pedestrians. Walking is a hot topic in traffic research in recent years [1]. Walking is [...]

The environmental traffic assignment problem in the bimodal network with electric vehicles (EVs) and gasoline vehicles (GVs) has become a hot topic recently. However, few previous works consider the psychological difference between EV users and GV users in terms of environmental awareness , in order to fill in this research gap, we formulate an environment-oriented mixed-behavior user equilibrium (EMUE) model to distinguish the route choice criterion between EV users and GV users by considering the Pro-environmental behavior (PEB) of EV users.. We formulate a bi-objective optimization model and propose a differentiable road pricing scheme for EV and GV users to manage the congestion and emissions simultaneously. We investigate whether the differentiable road pricing can decentralize the Pareto-efficient flow as a unique mixed-behavior equilibrium. Moreover, we conduct numerical experiments based on the Sioux Falls network and the existing urban network in Qianhai Zone to evaluate the effectiveness of the proposed pricing scheme, models and algorithms. [ABSTRACT FROM AUTHOR]

A study undertaken by the company Gartner (https://www.gartner.com/) suggests that customer effort is the strongest driver of customer loyalty, or disloyalty. There has been a large amount of focus on the M (mobility) in Mobility as a Service (MaaS), which has in many senses dwarfed the focus on S (service). [Extracted from the article]

Increasing concerns about air pollution and the promise of enhancing energy security have stimulated the growth of electric vehicles (EVs) worldwide. Compared with gasoline vehicles (GVs), EVs have no emissions and are more environmentally friendly to the sustainable transportation system. Since these two types of vehicles with different emission externalities and observable differences, in this paper, we propose a differentiable road pricing for EVs and GVs to simultaneously manage congestion and emissions by establishing a two-class bi-objective optimization (TCBO) model. First, we investigate whether the differentiable road pricing can induce user equilibrium pattern into a unique pareto-efficient pattern. Then performance of the bi-criteria system optimal is measured by bounding the deviation gap of the Pareto frontier. Specifically, we bound how far the total system travel time and total system emissions at a given Pareto optimum can deviate from their respective single-criterion based system optimum. Finally, we investigate the maximum efficiency gain of the bi-criteria system achieved through implementing differentiable road pricing by comparing total system travel time and total system emissions under two states. After defining two types of price of anarchy (POA), the theoretical bound for the worst possible ratio of total system travel time\ total system emissions in user equilibrium state to the total system travel time\ total system emissions in Pareto-efficient state is derived out. In order to validate the feasibility of theoretical bound, we conduct case studies to calculate the numerical bound of POA based on two Chinese cities: Shenzhen and Lasa. Overall, quantifying the maximum efficiency of differentiable road pricing is beneficial for improving the network designing, policy implementation and social efficiency with regard to congestion and emissions caused by EV and GV users. [ABSTRACT FROM AUTHOR]

U‐shaped automated container terminals (ACTs) represent a strategic design in port infrastructure that facilitates simultaneous loading and unloading operations. This paper addresses the challenges of scheduling multiple types of equipment, such as dual trolley quay cranes (DTQCs), automated guided vehicles (AGVs), double cantilever rail cranes (DCRCs), and external trucks (ETs) in U‐shaped ACTs. This paper proposes a mixed integer linear programming model for optimizing the multiple equipment scheduling, aiming to minimize container completion time and AGV waiting time simultaneously. This paper customizes a hybrid genetic‐cuckoo optimization algorithm (HGCOA) with double‐point crossover and Lévy flight Cuckoo search strategies. Extensive numerical results show that the proposed HGCOA outperforms the benchmark genetic algorithms in terms of solution quality and computational time while significantly improving efficiency without substantial sacrifices in solution quality compared with the exact solution method. Overall, this study presents a promising solution for enhancing coordination and operation efficiency in U‐shaped ACTs [ABSTRACT FROM AUTHOR]

As nontechnical losses in power systems have recently become a global concern, electricity fraud detection models attracted increasing academic interest. The wide application of smart meters has offered more possibility to detecting fraud from user's consumption patterns. However, the performances of existing consumption-based electricity fraud detection models are still not satisfactory enough for practice, partly due to their limited ability to handle high-dimensional data. In this paper, a deep-learning-based model is developed for detecting electricity fraud in the advanced metering infrastructure, namely, the multitask feature extracting fraud detector (MFEFD). The deep architecture has brought MFEFD a powerful ability to handle high-dimensional input, through which consumption patterns inside load profiles can be effectively extracted. Another challenge is that the insufficiency of labeled data has restricted the generalization of existing models since they are mostly based on supervised learning and labeled data. MFEFD is trained in a semisupervised manner, in which multitask training was implemented to combine the supervised and unsupervised training, so that both the knowledge from unlabeled and labeled data can be made use of. Real-world-data-based case studies have demonstrated MFEFD's high detection performance, robustness, privacy preservation, and practicability.