Search

The fuel consumption and greenhouse gas (GHG) emission patterns of taxis are in accordance with the urban structure and daily travel footprints of residents. With taxi trajectory data from the intelligent transportation system in Xi'an, China, this study excludes trajectories from electric taxis to accurately estimate GHG emissions of taxis. A gradient boosting decision tree (GBDT) model is employed to examine the nonlinear influence of the built environment (BE) on the GHG emissions of taxis on weekdays and weekends in various urban areas. The research findings indicate that the GHG emissions of taxis within the research area exhibit peak levels during the time intervals of 7:00–9:00, 12:00–14:00, and 23:00–0:00, with notably higher emission factors on weekends than on weekdays. Moreover, a clear nonlinear association exists between BE elements and GHG emissions, with a distinct impact threshold. In the different urban areas, the factors that influence emissions exhibit spatial and temporal heterogeneity. Metro/bus/taxi stops density, residential density, and road network density are the most influential BE elements impacting GHG emissions. Road network density has both positive and negative influences on the GHG emissions in various urban areas. Increasing the road network density in subcentral urban areas and increasing the mixed degree of urban functions in newly developed urban centers to 1.85 or higher can help reduce GHG emissions. These findings provide valuable insights for reducing emissions in urban transportation and promoting sustainable urban development by adjusting urban functional areas. [ABSTRACT FROM AUTHOR]

Existing studies have limited evidence about the complex nonlinear impact mechanism of road network topology and built environment on bike-sharing systems' greenhouse gas (GHG) emission reduction benefits. To fill this gap, we examine the nonlinear effects of road network topological attributes and built environment elements on the potential GHG emission reduction of dockless bike-sharing (DBS) trips in Shenzhen, China. Various methods are employed in the research framework of this study, including a GHG emission reduction estimation model, spatial design network analysis (sDNA), gradient boosting decision tree (GBDT), and partial dependence plots (PDPs). Results show that road network topological variables have the leading role in determining the potential GHG emission reduction of DBS trips, followed by land use variables and transit-related variables. Moreover, the nonlinear impacts of road network topological variables and built environment variables show certain threshold intervals for the potential GHG emission reduction of DBS trips. Furthermore, the impact of built environment on the potential GHG emission reduction of DBS trips is moderated by road network topological indicators (closeness and betweenness). Compared with betweenness, closeness has a greater moderating effect on built environment variables. These findings provide empirical evidence for guiding bike-sharing system planning, bike-sharing rebalancing strategy optimization, and low-carbon travel policy formulation. [ABSTRACT FROM AUTHOR]

Over the past few decades, taxi drivers' income has received extensive attention from scholars. Previous studies have investigated the factors affecting taxi drivers' income from multiple perspectives. However, less attention has been paid to road network topology, which has a direct impact on taxis' operation efficiency and drivers' income. To fill this gap, this paper examines the relationship between taxi drivers' income and urban road network topology; we employed various methods, namely, spatial design network analysis (sDNA), bivariate Moran's I, and geographically weighted regression (GWR). The results show the following. (1) The total order income (TOI) of taxi drivers has a certain degree of positive spatial correlation with closeness and betweenness. (2) The impact of urban road network topology on the average order income (AOI) of taxi drivers is stable. Specifically, closeness and betweenness have significant impacts on the AOI of taxi drivers at the medium and larger scales. (3) Closeness has a negative impact on the AOI of taxi drivers, and betweenness has a positive impact on the AOI of taxi drivers. (4) Compared with betweenness, the impact of closeness on the AOI of taxi drivers is greater and more stable. These findings can provide useful reference values for the development of policies aimed at improving both taxi drivers' income and urban road network efficiency. [ABSTRACT FROM AUTHOR]

The optimal delivery route problem for truck–drone delivery is defined as a traveling salesman problem with drone (TSP-D), which has been studied in a wide range of previous literature. However, most of the existing studies ignore truck waiting time at rendezvous points. To fill this gap, this paper builds a mixed integer nonlinear programming model subject to time constraints and route constraints, aiming to minimize the total delivery time. Since the TSP-D is non-deterministic polynomial-time hard (NP-hard), the proposed model is solved by the variable neighborhood tabu search algorithm, where the neighborhood structure is changed by point exchange and link exchange to expand the tabu search range. A delivery network with 1 warehouse and 23 customer points are employed as a case study to verify the effectiveness of the model and algorithm. The 23 customer points are visited by three truck–drones. The results indicate that truck–drone delivery can effectively reduce the total delivery time by 20.1% compared with traditional pure-truck delivery. Sensitivity analysis of different parameters shows that increasing the number of truck–drones can effectively save the total delivery time, but gradually reduce the marginal benefits. Only increasing either the truck speed or drone speed can reduce the total delivery time, but not to the greatest extent. Bilateral increase of truck speed and drone speed can minimize the delivery time. It can clearly be seen that the proposed method can effectively optimize the truck–drone delivery route and improve the delivery efficiency. [ABSTRACT FROM AUTHOR]

Featured Application: This study may promote the risk management and control level under high-density pedestrian flow, which is significant for the expansion of the scope and field of early warning applications. This study aims at evaluating the congestion level of pedestrians in metro stations. Twelve hours (4 h × three facilities) of video data were collected in the channel, stairway, and platform in a metro station in the city of Ningbo, China. The indicator of GPC (grade of pedestrian crowd) was proposed to quantify the congestion level of pedestrians. Four levels of congestion (level I, level II, level III, and level IV) were determined based on the GPC. A normal-cloud (NC) model was proposed and calibrated for the evaluation of three facilities including channel, stairway, and platform. The evaluation results showed that the GPC of L1-L2 and L2-L1 in channel are level II and level I, respectively. The GPC of upward and downward of stairway are level III and level I. The GPC of platform is level IV. Crowd management countermeasures were proposed for the management of pedestrians in metro station. [ABSTRACT FROM AUTHOR]

The networked operation of Urban Rail Transit (URT) brings the new challenge of network-wide maintenance. This research focuses on the URT Network-Wide Maintenance Decision-Making Problem (URT-NMDP), including regionalized maintenance network design and maintenance resource allocation. In this work, we proposed a bi-objective integer programming model that integrates the characteristics of set coverage and P-median models, resulting in the regionalized maintenance network design model. Some critical factors are considered in the model, such as the importance of node, the maximum failure response time, and maintenance guarantee rules. We designed a NSGA-II based algorithm to solve the model. Moreover, due to the uncertainty of failures in the URT network, we developed the method of allocating maintenance resources based on Monte Carlo simulation to strengthen the reliability of the regionalized maintenance network. With the model and algorithm presented in this work, we obtained Pareto optimal solutions of URT-NMDP, i.e., URT network-wide maintenance planning schemes, which include the number and location of maintenance points, the allocation of demand points, and the amount of maintenance units. Finally, a real-world case is studied to evaluate the operating performance of these schemes for verifying the method in our paper. The results of the case study demonstrate that the reasonable and tested-in-practice maximum failure response time is the precondition for the efficient URT maintenance network. The maintenance scheme considered the weighted importance of node shows the optimal performance, with the shortest overall maintenance path and the minimum average failure response time and investment cost on maintenance resources. [ABSTRACT FROM AUTHOR]

An optimal restoration strategy for supply chain networks can efficiently schedule the repair activities under resource limits. However, a wide range of previous studies solve this problem from the perspective of cost-effectiveness instead of a resilient manner. This research formulates the problem as a network maximum-resilience decision. We develop two metrics to measure the resilience of the supply chain networks, i.e., the resilience of cumulative performance loss and the resilience of restoration rapidity. Then, we propose a bi-objective nonlinear programming model, which aims to maximize the network resilience under the budget and manpower constraints. A modified simulated annealing algorithm is employed to solve the model. Finally, a testing supply chain network is utilized to illustrate the effectiveness of the proposed method framework. The results show that the optimal restoration schedule generated by the proposed model is a tradeoff between the cumulative performance loss and the restoration rapidity. Additionally, the sensitivity analysis of parameters indicates that decision-maker's preference, tolerance factor of delivery time, number of work crews, and availability of budget all have significant impacts on the restoration schedule. [ABSTRACT FROM AUTHOR]

An optimal pavement maintenance strategy can keep the pavement performance at a high level under budget constraint. However, the impact of changes in traffic distribution caused by maintenance actions on user costs is rarely investigated in existing approaches. This research aims to solve the optimization of pavement maintenance strategy using a multi-stage dynamic programming model combined with the stochastic user equilibrium model, which can simulate the dynamic traffic distribution in the life cycle. To deal with the proposed model, a heuristic iterative algorithm is put forward. Ultimately, a hypothetical network is established to test the model and algorithm. The testing results prove that the proposed framework has an advantage in assessing user costs comprehensively and can provide an effective and optimal pavement maintenance strategy in a 30-year life cycle, which improves the efficiency of budget and pavement conditions. Additionally, this research provides quantitative evidence of interdependency in a road network, i.e., pavement maintenance actions on links can interfere with the user costs and traffic flow distribution in the whole network, which should be taken into account in pavement maintenance decision-making. [ABSTRACT FROM AUTHOR]

With a proactive loan policy to raise construction funds, a large number of toll freeways have been built in Mainland China in the past three decades. However, it brought about a long-term heavy debt burden for most provincial governments. To ensure financial sustainability of toll freeways, an accurate and appropriate debt risk evaluation has become necessary. This research aims to explore debt risk factors and calculate the overall debt risk levels of toll freeways using the grey approach. Debt risk factors were identified as belonging to five categories—debt scale, debt structure, debt management, external environment, and solvency—and three new debt risk factors were added for specific concern of toll freeways—toll revenue, free cash flow, and earnings before interest, tax, depreciation, and amortization (EBITDA) margin. Debt risk levels of toll freeways in 29 provinces in Mainland China were evaluated by the proposed method and classified into three groups–low debt risk, medium debt risk, and high debt risk according to grey possibility degree ranges. Calculation results show that six provinces have low debt risk, 10 provinces have medium debt risk, and 13 provinces have high debt risk. Additionally, some specific policies to reduce toll freeway debt risk were provided based on the evaluation findings. [ABSTRACT FROM AUTHOR]

Existing Dynamic Traffic Assignment (DTA) models assign traffic flow with the principle of travel time, which are easy to distribute most of the traffic flows on the shortest path. A serious unbalance of traffic flow in the network can speed up pavement deterioration of highways with heavy traffic, which influences the sustainability of pavement performance and increases maintenance expenditures. The purpose of this research is to obtain a more optimized traffic assignment for pavement damage reduction by establishing a multi-objective DTA model with the objectives of not only minimum travel time but minimum decline of Present Serviceability Index (PSI) for pavements. Then, teaching-learning-based optimization (TLBO) algorithm is utilized to solve the proposed model. Results of a case study indicate that a more balanced traffic flow assignment can be realized by the model, which can effectively reduce average PSI loss, save maintenance expenditures, extend pavement service life span, save fuel consumption and reduce pollutant emissions in spite of a little increase of average travel time. Additionally, sensitivity of weight factor for the two objective functions is analyzed. This research provides some insights on methods on sustainable pavement performance. [ABSTRACT FROM AUTHOR]

In this work, a 2-D subaperture polar format algorithm (PFA) based on steppedchirp signal is proposed. Instead of traditional pulse synthesis preprocessing, the presented method integrates the pulse synthesis process into the range subaperture processing. Meanwhile, due to the multi-resolution property of subaperture processing, this algorithm is able to compensate the space-variant phase error caused by the radar motion during the period of a pulse cluster. Point target simulation has validated the presented algorithm.