Truck Flow Research Articles

The economic cost of locking down like China: Evidence from city-to-city truck flows

Containing the COVID-19 pandemic by non-pharmacological interventions is costly. Using high-frequency, city-to-city truck flow data, this paper estimates the economic cost of lockdown in China, a stringent yet effective policy prior to the Omicron surge. By comparing the truck flow change in the cities with and without lockdown, we find that a one-month full-scale lockdown causally reduces the truck flows connected to the locked down city in the month by 54%, implying a decline of the city’s real income with the same proportion in a gravity model of city-to-city trade. We also structurally estimate the cost of lockdown in the gravity model, where the effects of lockdown can spill over to other cities through trade linkages. Imposing full-scale lockdown on the four largest cities in China (Beijing, Shanghai, Guangzhou, and Shenzhen) for one month would reduce the real national GDP by 8.7%, of which 8.5% is contributed by the spillover effects.

Spatiotemporal differentiation calendar for car and truck flow on expressways: A case study of Jiangsu, China

The transport objects and demands of cars and trucks differ, leading to different spatiotemporal characteristics in their flow patterns. Existing studies on expressway flow are mainly focused on the overall traffic, however, a decomposition analysis of cars and trucks is necessary to accurately explore traffic prediction, management, and dynamic toll policy. This study presents calendars and spatial distribution of car and truck flow based on toll fee data in Jiangsu Province, China. Additionally, the influencing factors are explored by using Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. The results show that: (1) car flow follows a regular pattern of low from Monday–Thursday and high from Friday–Sunday, while high from Monday–Friday and low from Saturday–Sunday for truck flow. This law is not obvious in the total flow calendar. (2) The car flow predominantly accumulates in the 0–20 km range, while the truck flow is concentrated within the 20–40 km range. (3) The spatial distributions are stable for both car and truck flow, characterized by higher flows in the southern part of Jiangsu and lower in the northern region. Gaps in truck flow between cities are greater than those in car flow, narrowing during holidays/festivals than on weekdays or weekends. (4) private vehicle possession and travel time to Shanghai are the main influencing factors on car flow during different periods, the number of research institutions and the added value of tertiary industry are positively correlated on weekdays and weekends, respectively. Total retail sales of consumer goods is the main factor affecting truck flow during different periods, total import and export goods can be attributed to weekdays, while per capita disposable income affects weekends and festivals. Finally, the findings of this study offer insights for the optimal implementation of toll policies.

Quantifying the overall spatial distribution characteristics of urban heavy truck trips: The case of China

Urban freight systems serve as an essential part of city livability. Heavy trucks are critical for urban freight system operation. Although understanding the overall spatial distribution characteristics of heavy truck trips is important for urban freight system planning and management, quantifying the spatial distribution characteristics of heavy truck trips remains scarce. In this study, we employ two indices, the flow hierarchy index and the urban centrality index, to quantify the overall spatial distribution characteristics of heavy truck trips from two perspectives: the heavy truck flow hierarchical organization and outflow spatial distribution centrality. We use these approaches to quantify the overall spatial distribution characteristics of heavy truck trips for typical Chinese cities and classify them into four patterns. We further analyze the characteristics and underlying mechanism of each pattern and then calculate the overall heavy truck trip efficiencies for cities in different patterns. Our research offers a framework for quantifying the overall spatial distribution characteristics of urban heavy truck trips and provides policymaking references for the planning and management of urban freight systems.

Optimizing Sugar Manufacturing: A Hybrid Simulation-Based Approach and MCDM for Efficient Decision Making

Efficient truck flow is essential for the efficient operation of a factory and the distribution of its products. This study demonstrated methods to improve truck loading times and overall efficiency at a major sugar manufacturing facility in the Middle East. The objective was to reduce truck waiting times at loading units and increase capacity. The data were collected through questionnaires, observations, and interviews with stakeholders. A simulation software was employed to analyze truck activity at loading stations at the factory. Multi-criteria decision making (MCDM) tools, AHP and TOPSIS, addressed five primary criteria and nine sub-criteria to assist in identifying, evaluating, and ranking feasible solutions. The study suggested different utilization of the various factory loading platforms at different times of the day. The findings from this study emphasize the importance of simulation-based approaches supplemented with decision-making processes to improve efficiency in sugar manufacturing facilities that may have broader applications in the factories of other industries. The study highlights remarkable improvements in operational efficiency, as seen in Alternative 4 substantial 27.9% enhancement, resulting in cost savings and time efficiency. By implementing these findings, factories can enhance their truck flow management system, reduce waiting times, increase capacity utilization, optimize resource allocation, and improve overall efficiency.

Diversification, vertical integration and economic resilience: evidence from intercity truck flows during COVID-19 in China

AbstractThis article examines economic resilience by combining high-frequency truck flows and the lockdown policy shock during COVID-19 in China. We discover that the truck flows in regions with higher levels of diversification and vertical integration see a smaller decrease in response to the COVID-19 shock. Dynamically, such moderating effects of diversification and vertical integration get smaller with the recovery of interregional economic linkages. Diversification and integration also mitigate the negative impact from nonlocal infection cases. The association between the industrial structure attributes and economic resilience is more prominent in regions with lower centrality in the nationwide intercity truck flow network.

RoadAI: Reducing emissions in road construction

This challenge aims to reduce CO2 emissions from Norwegian construction machines by utilizing data-driven approaches.Skanska Norge AS provides a wealth of data from their construction sites, including GPS data, machine data, vibration data, and drone maps. The challenge focuses on using this data to optimize road construction processes for increased sustainability. Participants are tasked with developing feasible solutions to reduce idle time, optimize dump truck flow, minimize unnecessary driving, and improve driving styles. The evaluation criteria consider the novelty of the algorithms, feasibility of implementation, and the sustainable impact. The competition encourages innovative approaches to leverage data for sustainable construction practices.

Agent-Based Investigation of Charger Queues and Utilization of Public Chargers for Electric Long-Haul Trucks

This paper investigates the charging needs and charger utilisation of a system of battery electric trucks and their public fast chargers, along the highway between the Swedish cities of Helsingborg and Stockholm, in a possible future scenario in which all trucks are battery electric. The system is investigated via an agent-based model which simulates a typical day with current levels of truck traffic on the road. The traffic flow is based on hourly truck flow data during a day and the annual flow of trucks. The findings indicate major potential for a well-functioning system of public chargers for trucks, with high utilisation, few queuing problems at charging stations, cheap public fast charging and a robust response to queues caused by peaks or increases in the traffic flow. The number of 900 kW chargers along the road needed to achieve this has been estimated at 140, and the utilisation factor for the chargers is predicted to be 30%.

Real-Time Scheduling of Autonomous Mining Trucks via Flow Allocation-Accelerated Tabu Search

Material transportation is an essential process in mining operations and accounts for a large portion of total energy consumption. With the development of automation technology, autonomous mining trucks have been deployed in open-pit mines to reduce transportation costs. Compared to manned mining trucks, autonomous trucks are more appropriate for automatic scheduling techniques due to their high informatization, high-precision perception, and sophisticated control. However, the existing truck scheduling research does not exploit autonomous trucks’ strengths to perform more delicate control, but still takes a traditional approach toward autonomous trucks. In this paper, we propose a mixed-integer programming model for joint optimization of autonomous trucks trips and speeds to minimize energy consumption. To solve the proposed scheduling model, a novel tabu search algorithm is proposed, which consists of two parts: an improved flow allocation model with matching factor is formulated in the upper part to determine the optimal truck flow, and in the lower part a tabu search procedure guided with the optimal flow is developed. Based on the mathematical model and solution technique, we also propose a real-time scheduling system of autonomous trucks for the stochastic and dynamic mining environment. A coal mine in Inner Mongolia, China, for example, we verified the effectiveness of the proposed truck scheduling model and real-time scheduling approach. We demonstrate that the proposed allocation model effectively accelerates the tabu search procedure, and for short-term decisions, the proposed solution technique satisfies the computational requirements of the real-time scheduling system.

Simulation of the Impacts of a Zero-Emission Zone on Freight Delivery Patterns in Rotterdam

As part of a broader vision for emission-free city logistics, the city of Rotterdam plans to introduce a zero-emission zone in combination with urban consolidation centers (UCCs) on the outskirts of the city to generate a shift to zero-emission vehicles. For the design of this zero-emission zone, several research questions arise that require a systematic analysis of the impacts of the transition scenarios on freight demand patterns, the use and market shares of new (zero-emission) vehicles, and the impacts of truck flow and emissions. As a case study, we implemented heterogenous transition scenarios for each logistic segment into the Tactical Freight Simulator from the HARMONY project and analyzed the systemwide impacts. This model is multiagent, empirical, and shipment based and simulates long-term tactical choices (distribution channel choice, shipment size and vehicle type choice, sourcing) and short-term tactical choices (tour formation, delivery times). Results showed that the impact of UCCs is not trivial: we observed a small increase in vehicle kilometers traveled overall of +0.25%, which can be attributed to the rerouting of shipments through the UCCs. Calculations confirmed that emissions reduced dramatically, by 90%, inside the zero-emission zone. At the city scale this corresponds to a reduction of almost 10%, as most freight-related traffic is generated by the port and involves long-haul heavy goods vehicle transport that does not enter the city center. At a regional level, impact reduction was very small. More measures are needed if more ambitious reductions in emissions are to be achieved.

A Multifeatures Spatial-Temporal-Based Neural Network Model for Truck Flow Prediction

The majority of studies on road traffic flow prediction have focused on the flow of passenger cars or the flow of traffic as a whole, which ignore the significant impact of trucks with different sizes and operational characteristics on traffic flow efficiency. Therefore, in this paper, we focus on truck traffic flow and propose a Multifeatures Spatial-Temporal-Based Neural Network model (M-BiCNNGRU) to improve its prediction. The proposed model not only comprises conventional temporal characteristics and spatial relationships but also includes a range of multifeatures. These multifeatures include policy limit, optimal time delay, road resistance, and traffic congestion state. The impacts of upstream and downstream road sections are considered on the spatial relationship by using a Convolutional Neural Network (CNN). A Bidirectional Gated Recurrent Unit (Bi-GRU) is employed to account for the temporal characteristics. To evaluate the proposed model, traffic flow data were collected from a major expressway in Beijing and the results were compared with those derived from existing models. The results show that the prediction accuracy of the BiCNNGRU model, with spatial-temporal characteristics, and the M-BiGRU model, with multifeatures and temporal, is, respectively, 4.13% and 2.15% greater than that of the Bi-GRU model, with temporal characteristics. The prediction accuracy of the proposed M-BiCNNGRU model is 92.86%, which is 7.12% greater than that of the Bi-GRU model and 13.83% greater than that of the Support Vector Regression (SVR) model. In general, therefore, the proposed M-BiCNNGRU model, which combines multifeatures, temporal characteristics, and spatial relationships, can significantly improve accuracy in predicting truck traffic flow.

Regional Logistics Network Design in Mitigating Truck Flow-Caused Congestion Problems

Truck flow plays a vital role in urban traffic congestion and has a significant influence on cities. In this study, we develop a novel model for solving regional logistics network (RLN) design problems considering the traffic status of the background transportation network. The models determine not only the facility location, initial distribution planning, roadway construction, and expansion decisions but also offer an optimal solution to the logistics network service level and truck-type selections. We first analyze the relationship between the urban transportation network and the RLN design problem using real truck data and traffic flow status in a typical city. Then, we develop the uncover degree function (UDF), which reflects the service degree of the RLN and formulates based on an impedance function. Subsequently, the integrated logistics network design models are proposed. We model the RLN design problem as a minimal cost problem and design double-layer Lagrangian relaxation heuristics algorithms to solve the model problems. Through experiments with data from the six-node problem and Sioux-Falls network, the effectiveness of the models and algorithms is verified. This study contributes to the planning of regional logistics networks while mitigating traffic congestion caused by truck flow.

Weight-categorized truck flow estimation: A data-fusion approach and a Florida case study

Knowledge of spatial distributions of weight-categorized truck flows in a region is critical to the understanding of movements of empty or partially-loaded trucks and devising appropriate strategies to reduce empty or partially-loaded truck flows and improve truck utilization efficiency in the region. However, such disaggregated information cannot be directly obtained from existing data sources and models. In this paper, we propose a compact model for estimating weight-categorized truck origin–destination (OD) flows and link-level truck counts by fusing several freight datasets. The proposed model minimizes the squared errors between the estimated and observed truck OD flows and link volumes considering the flow conservation of trucks and commodity weights. To illustrate a real-world application of this model, a case study is conducted to estimate the spatial distribution of empty or partially-loaded truck flows into, within, and out of the State of Florida. With the case study results, high production- and attraction-zones of empty or partially-loaded truck trips are also identified. Such results can potentially inform freight planning and policy decisions to learn spatial patterns of empty or partially-loaded backhauling truck flows and devise countermeasures to reduce such flows and improve freight transportation efficiency. This is particularly relevant to a terminal state such as Florida that has large volumes of back-hauling truck flow.

Study on Optimization of Coal Truck Flow in Open‐Pit Mine

A semicontinuous process system consisting of a single‐bucket excavator, truck, crushing station, and belt conveyor is the main coal mining process system of a large‐scale hard coal open‐pit mine. Through analyzing the coal mining production process, the key issues of coal mining truck flow optimization are obtained. Statistical method of using triangular fuzzy numbers analyzes the key time parameters of coal mining truck flow. Taking one shift, the minimum expected value of number of trucks, as the objective function, the fuzzy expectation of the coal mining semicontinuous process system is established with the constraints of the truck flow continuity at the loading and unloading point, the production capacity of the electric shovel, the production capacity of the crushing station, coal quality, and coal mining production tasks. The truck flow planning model is solved using particle swarm intelligence algorithm. The simulation results show that the result of truck flow planning can effectively reduce truck number, truck dispatching number, transportation costs, and truck queuing. The fuzzy expected truck flow planning model established by the study is suitable for solving the problem of optimizing and matching the production capacity in the process which links electric shovel, truck, and crushing station. It can effectively improve the production efficiency of electric shovel mining, truck transportation, and crushing station.

Truck Traffic Flow Prediction Based on LSTM and GRU Methods With Sampled GPS Data

Given the enormous traffic issues, such as congestion and crashes, resulting from the conflicts between trucks and passenger cars, an accurate and reliable prediction of truck traffic flow is needed to enhance the traffic flow efficiency and safety in the mixed traffic condition. Enabled by emerging sensing technologies, the GPS data become available and will reveal some insights to improve the understanding of truck traffic flow prediction. In the paper, a novel method of truck traffic flow prediction is proposed by using sampled GPS data in the roadway network. The proposed method consists of two phases, which are expansion and prediction. In the data expansion phase, a piece-wise constant coefficient method is designed to minimize errors between the sampled truck flow and the actual truck flow, where the coefficients are determined according to road grades and traffic flow size. In the prediction phase, Long Short Term Memory (LSTM) and Gated Recursive Unit (GRU) neural network methods are first time employed to improve the prediction accuracy. Considering that the sequence of the expansion and prediction could have different prediction performance, approaches using both `previous-prediction', `post-expansion' and `previous-expansion', `post-prediction' were used and the results compared with the survey data from traffic flows. The results demonstrate that LSTM and GRU have a superior performance compared to existing approaches using SRV and ARIMA for truck traffic flow prediction. For the whole prediction period, LSTM has better prediction results than GRU overall with an accuracy which is 4.10% better than that of GRU. Furthermore, the accuracy of the `previous-prediction', `post-expansion' is 8.26% greater than that of the `previous-expansion', `post-prediction'.

What will autonomous trucking do to U.S. trade flows? Application of the random-utility-based multi-regional input–output model

This study anticipates changes in U.S. highway and rail trade patterns following widespread availability of self-driving or autonomous trucks (Atrucks). It uses a random-utility-based multiregional input–output model, driven by foreign export demands, to simulate changes in freight flows among 3109 U.S. counties and 117 export zones, via a nested-logit model for shipment or input origin and mode, including the shipper’s choice between autonomous trucks and conventional or human-driven trucks (Htrucks). Different value of travel time and cost scenarios are explored, to provide a sense of variation in the uncertain future of ground-based trade flows. Using the current U.S. Freight Analysis Framework (FAF4) data for travel times and costs—and assuming that Atrucks lower trucking costs by 25% (per ton-mile delivered), truck flow values in ton-miles are predicted to rise 11%, due to automation’s lowering of trucking costs, while rail flow values fall 4.8%. Rail flows are predicted to rise 6.6% for trip distances between 1000 and 1500 miles, with truck volumes rising for all other distance bands. Introduction of Atrucks favors longer truck trades, but rail’s low price remains competitive for trade distances over 3000 miles. Htrucks continue to dominate in shorter-distance freight movements, while Atrucks dominate at distances over 500 miles. Eleven and twelve commodity sectors see an increase in trucking’s domestic flows and export flows, respectively. And total ton-miles across all 13 commodity groups rise slightly by 3.1%, as automation lowers overall shipping costs.

Quantitative Assessment on Truck-Related Road Risk for the Safety Control via Truck Flow Estimation of Various Types

Traffic conditions of truck flow is one of the critical factors influencing transportation safety and efficiency, which is directly related to traffic accidents, maintenance scheduling, traffic flow interruption, risk control, and management. The estimation of the truck flow of various types could be better to identify the irregular flow variation introduced by various trucks and quantitatively assessed the corresponding road risks. In this paper, the dynamics of truck flow are estimated first. The stochastic and uncertain trucks flow data are obtained in terms of small, medium, heavy, and the oversize truck type and regulated corresponding flow in the time series within five minutes. In order to dig the spatial-temporal correlations behind those data, the deep learning-based method is improved on the basis of the gated recurrent unit (GRU) to estimate the truck flow for various types. To quantitatively assess the truck-related effect for road risk, a multiple logistic regression method is further proposed to classify into safe, risky, and dangerous road risks levels. Different risk level could guide the traffic control and management and traffic information that broadcast drivers to help them to choose travel route. The proposed prediction of the road risk is tested in the randomly selected road segment and shows superior compared to other methods. This could promote road safety in the development of intelligent transport system (ITS).

Evaluation of the spatial pattern of logistics facilities using urban logistics land-use and traffic simulator

Despite the growing research interests on the spatial restructuring of logistics facilities that occurred in many cities around the world, the relationship between the spatial pattern of logistics land use and the level of externalities is far from being elucidated. We use the Urban Logistics Land-use and Traffic Simulator (ULLTRA-SIM), developed for the Tokyo Metropolitan Area, to evaluate the level of externalities that accompany different spatial distribution patterns of logistics facilities. The ULLTRA-SIM takes a novel approach to analyze the urban freight impacts through the simulations of logistics facility locations, urban logistics chains, and truck flow. The results indicate that, while the moderate concentration and deconcentration of logistics facilities do not significantly affect the level of externalities, scarcity of logistics facilities in or near the high demand locations exacerbates negative externalities. Also, the results of the simulations underscore the need for rigorous analysis in order to reduce negative externalities through logistics land use policies.

Achieving a smooth flow of fuel deliveries by truck to an urban biomass power plant in Helsinki, Finland – an agent-based simulation approach

ABSTRACT Power plants that use biomass for fuel can have heavy truck flow rates, especially during winter. From the viewpoint of power plant operations, it is important that this flow of trucks is smooth; a traffic jam can lead to a decrease in the efficiency of the plant or, at worst, to unwanted shutdowns. To study the effects of different logistics solutions on the truck flow in the reception area of a plant, an agent-based simulation model was created. The model mimics the movement of trucks at a fuel reception site. The case selected for this is a power plant, planned for construction in the Helsinki area of Finland. The model period was set as one month during winter time. The case studies the effects of a second weighing station, automatic sampling and the income flows of different truck types. The results show that a second weighing station alleviates potential problems with the truck flow, compared to only having one weighing station, but a more effective way to improve fuel reception would be to decrease the unloading time by using automatic sampling. It was found more beneficial to use high capacity trucks for fuel reception, as fewer trucks need to enter the plant. The study shows that agent-based modeling can be utilized as a study method in power plant fuel reception areas, where all elements affect each other due to the dynamic nature of the logistics.

An RS-GIS-Based ComprehensiveImpact Assessment of Floods—A Case Study in Madeira River, Western Brazilian Amazon

Geographical information systems-based methods can be handled as powerful tools in assessing and quantifying impacts and, thus, supporting strategies for disaster risk reduction (DRR). This is particularly relevant on scenarios of global climate change and intensified increased human interventions on riverine systems. The Madeira River in Porto Velho city (Brazilian Amazon) is a good example of susceptible area to both of these factors. We take advantage of the 2014 flood, the largest recorded for this region, for combining remote sensing and geographic information system with socio, health, and infrastructure data to quantify spatially the flood impacts. Using high resolution airborne images, we applied a machine learning classification algorithm for detecting urban areas. Our results show that at the flood extent related to the highest river level at least 0.65 $\text {km}^{2}$ of urban area, 87 km of urban streets, four public schools, and two public health units were affected. More than 16 800 people suffered the impacts directly, and children represented 29.7% of them. Based on registered data, it was quantified that the city registered more than 20 cases of leptospirosis and the truck flow on the region decreased up to 92%. The spatially-explicit results of this letter are potential to guide strategies aiming to support decision-making for DRR.

Long distance truck tracking from advanced point detectors using a selective weighted Bayesian model

Truck flow patterns are known to vary by season and time-of-day, and to have important implications for freight modeling, highway infrastructure design and operation, and energy and environmental impacts. However, such variations cannot be captured by current truck data sources such as surveys or point detectors. To facilitate development of detailed truck flow pattern data, this paper describes a new truck tracking algorithm that was developed to estimate path flows of trucks by adopting a linear data fusion method utilizing weigh-in-motion (WIM) and inductive loop point detectors. A Selective Weighted Bayesian Model (SWBM) was developed to match individual vehicles between two detector locations using truck physical attributes and inductive waveform signatures. Key feature variables were identified and weighted via Bayesian modeling to improve vehicle matching performance. Data for model development were collected from two WIM sites spanning 26miles in California where only 11 percent of trucks observed at the downstream site traversed the whole corridor. The tracking model showed 81 percent of correct matching rate to the trucks declared as through trucks from the algorithm. This high accuracy showed that the tracking model is capable of not only correctly matching through vehicles but also successfully filtering out non-through vehicles on this relatively long distance corridor. In addition, the results showed that a Bayesian approach with full integration of two complementary detector data types could successfully track trucks over long distances by minimizing the impacts of measurement variations or errors from the detection systems employed in the tracking process. In a separate case study, the algorithm was implemented over an even longer 65-mile freeway section and demonstrated that the proposed algorithm is capable of providing valuable insights into truck travel patterns and industrial affiliation to yield a comprehensive truck activity data source.