Search Results (34)

The rapid development of the logistics industry has underscored the urgent need for efficient and sustainable urban freight systems. As a core component of freight systems, heavy-duty trucks (HDT) have been researched regarding surface-level descriptive statistics of their heterogeneities, such as trip volume, frequency, etc., but there is a lack of in-depth analyses of the spatial interaction between freight travel and freight functional clustering, which restricts a systematic understanding of freight systems. Against this backdrop, this study develops a data-driven framework to analyze HDT behavioral heterogeneity and its spatial interactions with a freight functional zone in Shanghai. Leveraging the high-frequency trajectory data of nearly 160,000 HDTs across seven types, we construct a set of regional indicators and employ hierarchical clustering, dividing the city into six freight functional zones. Combined with the HDTs’ application scenarios, functional characteristics, and trip distributions, we further analyze the spatial interaction between the HDTs and clustered zones. The results show that HDT travel patterns are not merely responses to freight demand but complex reflections of urban industrial structures, infrastructure networks, and policy environments. By embedding vehicle behaviors within their spatial and functional contexts, this study reveals a layered freight system in which each HDT type plays a distinct role in supporting economic activities. This research provides a new perspective for deeply understanding the formation mechanisms of HDT trip distributions and offers critical evidence for promoting targeted freight management strategies. Full article

Regional planning agencies increasingly rely on Spatial Economic Models (SEMs) to evaluate the impact of various policies. However, traditional SEMs often employ homogeneous technical coefficients (TCs) to represent technology patterns used by activities located in very different areas of a region, leading to misrepresentations of production and consumption behaviors, and consequently, inaccurate modeling results. To this end, we propose a Differential Spatial Economic Modeling (DSEM) framework that incorporates region-specific TCs for activities within Independent Planning Units (IPUs), such as provinces or cities, each characterized by unique economic, demographic, and technological features. The DSEM framework comprises three core components: (1) a regional economy model that forecasts activity totals for each IPU using economic and demographic forecasting model, supplemented by statistical analyses like the Gini index and K-means clustering to group activities from different IPUs into homogeneous ‘technology’ clusters based on their TCs; (2) a land use model that allocates IPU activity totals to corresponding traffic analysis zones (e.g., counties or districts) using the Differential Spatial Activity Allocation (DSAA) method. This determines the spatial distribution of commodities (such as goods, services, floor space, and labor) across exchange zones, balancing supply and demand to achieve spatial equilibrium in both quantity and price; and (3) a transport model that performs modal split and network assignment, distributing commodity trip origin–destination matrices across a multimodal transportation supernetwork (highways, railways, and waterways) using a probit-based stochastic user equilibrium assignment model. The proposed method is applied to a case study of the Yangtze River Economic Belt, China. The results demonstrate that the proposed DSEM yields better goodness-of-fit (R²) values between observed and estimated flows compared to the traditional aggregate SEM. This indicates a more precise and objective representation of spatial economic activities and technological patterns, thus resulting in improved estimates of freight flows for individual transportation modes and specific links. Full article

Road hazardous material transportation plays a critical role in road traffic management. Due to the dangerous nature of the cargo, hazardous material transportation trucks (HMTTs) have different route selection and driving characteristics compared to traditional freight trucks. These differences lead to unique travel and emission patterns, which in turn affect traffic management strategies and emission control measures. However, existing research predominantly focuses on safety aspects related to individual vehicle behavior, with limited exploration of the broader travel and emission characteristics of HMTTs. To bridge this gap, this study develops a comprehensive framework for analyzing the travel patterns and emissions of HMTTs. The methodology begins by applying a Gaussian mixture distribution model to identify vehicle stop points, eliminating biases associated with subjective settings. Origin–destination (OD) pairs are then determined through stop time clustering, followed by the extraction of travel characteristics using non-negative matrix factorization. Emissions are subsequently calculated based on the identified trip data. The relationship between emissions and land use characteristics is further analyzed using geographically weighted regression (GWR). Crucially, this study leverages data from the BeiDou Satellite Navigation System, focusing on HMTTs operating within Shanghai. The processed data reveal three distinct travel modes of HMTTs, categorized by spatiotemporal patterns: Daytime—Surrounding cities, Early morning—In-city, and Midnight—Scattered. Moreover, unlike other road vehicles, HMTT emissions are heavily influenced by industrial and company-related points of interest (POIs). These findings highlight the significant role of BeiDou Satellite Navigation System data in optimizing HMTT management strategies to reduce emissions and improve overall safety. Full article

Existing research on truck location prediction focuses on direct trajectory prediction and ignores the link between activity patterns and predictability, whereas the mode of operation is an important factor in the difference between activity trajectories, and analyzing the mode of operation can help to develop the next-location prediction algorithms to improve the efficiency of matching truckloads and to reduce costs. Our empirical study, based on 562,071 truck trip data in China, employs Fuzzy c-means (FCM) for clustering operational patterns in space, intensity, and stability dimensions. K-nearest neighbors (KNN), Back Propagation neural (BP) network, and Long Short-Term Memory (LSTM) predict the next truck locations in different modes. The results indicate that range-of-motion stability significantly influences predictability. Truckers with stable spatial activity exhibit the highest predictability, with 45% nearly achieving 100% predictability. Through cluster analysis of driving characteristics, we found that truck clusters are the most predictable because of their relatively small and low-intensity activities, with the percentage of samples with prediction accuracy above 90% reaching over 80%. This research not only characterizes the freight truck community but also aids algorithm optimization by revealing predictability factors for real-world applications. Full article

This paper establishes a multivariate function model for natural human load-carrying walking in some typical scenarios such as college equipment and material relocation by students and a large amount of identical freight relocation in commercial activities. For classified material relocation needs and constraints, we obtain the relationship between walking speed and load weight for a single person, as well as the time cost for different round trips. By establishing an integer programming model with the minimum total transportation time cost and shelf life as the objective function and the requirements of negative weight and speed as the constraint conditions, we reach the optimal item allocation methods considering time cost and shelf life. We discover that there is an approximate linear relationship between the change in natural walking speed and travel time when the load is small, thus obtaining the time cost of student transportation under different round-trip situations. The Monte Carlo simulation algorithm, which is more efficient compared with other methods such as the integer programming method, is used to obtain the optimal allocation scheme that meets the efficiency and quality requirements. The analysis methods and results can be used as guidance for task scheduling optimization for material relocation in educational organizations as well as commercial agencies. Full article

Expressway systems play a vital role in facilitating intercity travels for both passengers and freights, which are also a significant source of vehicle emissions within the transportation sector. This study investigates vehicle emissions from expressway systems using the COPERT model to develop multi-year emission inventories for different pollutants, covering the past and future trends from 2005 to 2030. Thereinto, an integrated SARIMA-SVR method is designed to portray the temporal variation of vehicle population, and the possible future trends of expressway vehicle emissions are predicted through policy scenario analysis. The Jiang–Zhe–Hu Region of China is taken as the case study to analyze emission control in expressway systems. The results indicate that (1) carbon monoxide (CO) and volatile organic compounds (VOCs) present a general upward trend primarily originating from passenger vehicles, while nitrogen oxides (NOx) and inhalable particles (PM) display a slowing upward trend with fluctuations mainly sourcing from freight vehicles; (2) vehicle population constraint is an effective emission control policy, but upgrading the medium- and long-haul transportation structure is necessary to meet the continuous growth of intercity trips. Expressway vehicle emission reduction effectiveness can be further enhanced by curtailing the update frequency of emission standards, along with the scrapping of high-emission vehicles. Full article

In the field of freight transport, the goal of sustainable development requires us to improve the efficiency of freight transport while reducing its negative impact on the environment, such as reducing carbon emissions and noise pollution. There is no doubt that changes in freight characteristics and volumes are compatible with the objectives of sustainable development. Thus, mining the travel distribution and freight volume of trucks has an important supporting role in the freight transport industry. In terms of truck travel, most of the traditional approaches are based on the subjective definition of parameters from the trajectory data to obtain trips for certain vehicle types. As for freight volume, it is mostly estimated through manual surveys, which are heavy and inaccurate. In this study, a data-driven approach is adopted to obtain trips from the trajectory data of heavy trucks. Combined with the traffic percentage of different vehicle types collected by highway traffic survey stations, the trips of heavy trucks are extended to all trucks. The inter-city and intra-city freight volumes are estimated based on the average truck loads collected at the motorway entrance. The results show a higher proportion of intra-city trips by trucks in port cities and a higher proportion of inter-city trips by trucks in inland cities. Truck loading and unloading times are focused in the early morning or at night, and freight demand in Shandong Province is more concentrated in the south. These results would provide strong support for optimizing freight structures, improving transportation efficiency, and reducing transportation costs. Full article

In today’s complex and dynamic transportation networks, increasing energy costs and adverse environmental impacts necessitate the efficient transport of goods or raw materials across a network to minimize all related costs through vehicle assignment and routing decisions. Vehicle routing problems under dynamic and stochastic conditions are known to be very challenging in both mathematical modeling and computational complexity. In this study, a special variant of the full-truckload vehicle assignment and routing problem was investigated. First, a detailed analysis of the processes in a liquid transportation logistics firm with a large fleet of tanker trucks was conducted. Then, a new original problem with distinctive features compared with similar studies in the literature was formulated, including pickup/delivery time windows, nodes with different functions (pickup/delivery, washing facilities, and parking), a heterogeneous truck fleet, multiple trips per truck, multiple trailer types, multiple freight types, and setup times between changing freight types. This dynamic optimization problem was solved using an intelligent multi-agent model with agent designs that run on vehicle assignment and routing algorithms. To assess the performance of the proposed approach under varying environmental conditions (e.g., congestion factors and the ratio of orders with multiple trips) and different algorithmic parameter levels (e.g., the latest response time to orders and activating the interchange of trip assignments between vehicles), a detailed scenario analysis was conducted based on a set of designed simulation experiments. The simulation results indicate that the proposed dynamic approach is capable of providing good and efficient solutions in response to dynamic conditions. Furthermore, using longer latest response times and activating the interchange mechanism have significant positive impacts on the relevant costs, profitability, ratios of loaded trips over the total distance traveled, and the acceptance ratios of customer orders. Full article

Recently, the main aim of the road freight transportation sector has been the establishment of a cost-effective and sustainable operation because it is one of the most environmentally damaging and most expensive elements of the supply chains’ activities. The efficiency improvement and optimization of these transport activities can result in significant cost savings, which lead to increased competitiveness of the transport companies. Two new methods were elaborated for the optimization of road freight transport activities; therefore, this research is very innovative and up to date. The elaborated methods are as follows: (1) A new calculation method for the precise prime cost pre-calculation of transport tasks in order to determine an accurate transport fee, thus ensuring the company’s profit; furthermore, the losses can be eliminated in order to provide competitiveness to the transport company. (2) A new optimization method for the refueling procedure of international transport trips in order to minimize the total fuel cost of the transport trips taking into consideration the different unit fuel prices at the different stations. Therefore, the elaborated optimization method on the one hand helps in selecting the optimal petrol station and, on the other hand, defines the optimal amount of fuel to be refueled. Based on the newly developed methods, two decision-supporting software applications were developed to establish more profitable and sustainable transportation. The added value of the developed calculation methods and software applications is that, recently, both the prime cost calculation and the fuel supply optimization have not been supported by software. This is the reason the two developed methods and two software applications are innovative and unique. The newly developed software applications were successfully implemented at transport companies. The correctness of both elaborated mathematical methods was validated using the developed software in real case studies. Full article

In order to optimize the functionality of automated guidance vehicles (AGVs) in logistics workshops, a wireless charging and task-based logistics intelligent dispatch system was developed based on the Internet of Things. This system aimed to improve freight efficiency in the workshop’s logistics system. The scheduling system successfully addressed the round-trip scheduling issue between AGVs and multiple tasks through two degrees of improvement: the application of AGVs and task path planning. To handle conflict coordination and AGV cluster path planning, a shortest path planning algorithm based on the A* search algorithm was proposed, and the traffic control law was enhanced. The initial population of genetic algorithms, which used greedy algorithms to solve problems, was found to be too large in terms of task distribution. To address this, the introduction of a few random individuals ensured population diversity and helped avoid local optima. Numerical experiments demonstrated a significantly accelerated convergence rate towards the optimal solution. Full article

The dynamic and complex interactions between the urban freight-transportation system and population, economy, traffic flow, fuel consumption, and environmental pollution, make policymaking in this system one of the fundamental challenges of urban management. In this regard, a systemic approach in urban freight-transportation system modelling should be considered to solve the problems of the system. One of the main problems of this system is the mismatch between the freight-transportation capacity and the total freight-transportation demand. Considering the lack of sufficient studies in the field of macro and quantitative modeling of this system, the main goal of this article is to model the urban freight-transportation system in order to identify the factors affecting the urban freight-transportation demand and capacity. The main focus of the research is to develop quantitative scenarios which balance the freight-transportation capacity and freight-transportation demand. The urban freight-transportation system is modelled by the System Dynamics (SD) approach and their basic behaviors; as well as this the results of some policy-making scenarios are simulated. The model is validated by the real data of Shiraz. Five quantitative scenarios are designed with two approaches of managing the freight-transportation demand and freight-transportation-capacity sectors. The scenarios are based on four control variables, including the distribution coefficient, trip numbers, vehicle capacity, and vehicle numbers. The simulation results show that the total gap between freight-transportation capacity and freight-transportation demand will decrease by optimizing each of the control variables. However, the combined scenario is the most applicable policy in order to maintain the balance between freight-transportation capacity and demand. Generally, the proposed model can be used to design different quantitative scenarios in order to optimize the freight-transportation system’s performance. This study can also help policymakers to manage the urban freight-transportation system more efficiently. Full article

The increasing market share of electric vehicles and the politically intended phase-out of the internal combustion engine require reliable and realistic predictions for future consumption and greenhouse gas emissions as a function of technological solutions. This also includes the consumption- and emission-intensive transport of goods. We consider both passenger vehicles and commercial vehicle traffic in our study and have investigated whether there are drive alternatives to the battery electric vehicle that enable uninterrupted trips with a long range, especially for regional delivery services and internationally active freight forwarders. To this end, we have analysed three system architectures and their expected technological progress until 2050: battery electric vehicles (BEV), fuel cell electric vehicles (FCEV), and internal combustion engine vehicles (ICEV) running on compressed natural gas (CNG). The latter case serves as a best-practice reference from a combustion technology perspective. The analysis is based on a validated and proven physical model and predicts that the BEV2050 will consume 3.5 times less energy and emit 15 times fewer greenhouse gases than the ICEV-CNG2020, whereas the FCEV2050 will consume 2.5 times less energy and emit 6.5 times fewer greenhouse gases than the ICEV-CNG2020 on the road (hilly terrain, transition season, and WLTP triple-mixed drive cycle). The advantages of the BEV result from the shorter drive train with lower total losses. Our results thus confirm the expected role of the BEV as the dominant drive technology in the future, and light vehicles with low-to-medium-range requirements will especially benefit from it. On the other hand, since the greenhouse gas emissions of the FCEV2050 are lower by a factor of 6.5 than those of the ICEV-CNG2020, it is reasonable to conclude that the FCEV can play a significant role in transport until 2050 when long distances have to be covered. Our model-based approach also allows us to determine the energy fractions of the acting physical forces and thus calculate the consumption shares: electric drive recuperation increases BEV and FCEV range by about 15% in 2020 and will increase it by about 20% in 2050, depending on drive technology and vehicle type. Air and rolling resistance contribute 20% each to the total consumption. The consumption of the accessories of modern vehicles with a share of about 10% of the total consumption cannot be neglected. Full article

Agricultural product circulation is an appropriate way to optimize the distribution of agricultural resources and maintain food safety. The seasonality of agriculture leads to seasonal variations in agricultural product circulation. Previous studies constructed origin–destination networks based on annual statistics to investigate the static structure of agricultural product circulation networks from a single view, failing to capture the seasonal and multi-dimensional characteristics in agricultural product circulation. This study presents a multi-view analytical framework used to investigate the seasonal characteristics of an agricultural product circulation network. First, agricultural product circulation networks in different seasons were constructed with mass freight trajectory data through trajectory mining technology. Then, the seasonal characteristics of agricultural product circulation were, respectively, analyzed from a macro-view (networks), meso-view (edges) and micro-view (nodes). A case study was conducted in Beijing, China. It is argued that: (1) The presented method for extracting agricultural trip chains based on massive freight trajectories is feasible for the construction of agricultural product circulation networks. (2) The agricultural product circulation networks in four seasons exhibit an obvious hierarchical and radial structure. South China has a higher network density in winter and spring, whereas northeast and northwest China are the opposite. (3) A total of 80% of the linkage strength is concentrated, on average, in 35.3% of city-pairs in four seasons, where the agglomeration effect and hub status of the linking cities is more prominent in summer and autumn. (4) A total of 316 cities form Beijing agricultural product circulation networks, 48.1% of which are mainly served by Beijing agricultural product circulation in winter and spring, which is 2.7 times more than cities served in summer and autumn. These findings extend the scientific understanding of the agricultural product supply chain from a dynamic and multi-dimensional view, which provides essential information for optimizing sustainable agri-food systems and ensuring food security. Full article

With the spread of electronic toll collection (ETC) and electronic payment, it is still a challenging issue to develop a systematic approach to investigate highway travel patterns. This paper proposed to explore spatial–temporal travel patterns to support traffic management. Travel patterns were extracted from the highway transaction dataset, which provides a wealth of individual information. Additionally, this paper constructed the analysis framework, involving individual, and temporal and spatial attributes, on the basis of the RFM (Recency, Frequency, Monetary) model. In addition to the traditional factors, the weekday trip and repeated rate were introduced in the study. Subsequently, various models, involving K-means, Fuzzy C-means and SOM (Self-organizing Map) models, were employed to investigate travel patterns. According to the performance evaluation, the SOM model presented better performance and was utilized in the final analysis. The results indicated that six groups were categorized with a significant difference. Through further investigation, we found that the random traveler occupied over 40% of the samples, while the commuting traveler and long-range freight traveler presented relatively fixed spatial and temporal patterns. The results were also meaningful for highway authority management. The discussion and implication of travel patterns to be integrated with the dynamic pricing strategy were also discussed. Full article

Integrating freight and public transport infrastructure can lead to providing economic feasibility to public transportation systems and reducing externalities related to urban freight transport. This can be achieved by sharing the infrastructure of freight and public transportation systems. Additionally, failed deliveries represent a major challenge in e-commerce. Lockers can address this problem and promote sustainable urban freight transport. This paper identified a locker network in a public transportation infrastructure. The framework considered scenarios built under the 15-min city concept, and the analysis is based on a case study in Jaraguá do Sul (Brazil, a mid-sized Brazilian city, and its conurbated area. The networks were found by solving a p-median problem, which minimised the maximum distance between the lockers and the population. The findings showed that, in the best scenario with 16 lockers, the population could reach the lockers within a 10-min cycling ride. Additionally, the results showed that the public transportation network provides a locker network to integrate freight and public transportation. The locker network is accessible to public transportation and micromobility users. With this solution, residents play an active role in last-mile deliveries. In addition, lockers can work as mini hubs for crowdshipping services. In addition to reducing urban delivery trips, this solution can encourage public transportation usage, which contributes to more sustainable cities. Full article