Truck Trips Research Articles

Optimized demand-based charging networks for long-haul trucking in Europe

Abstract Battery electric trucks (BETs) are the most promising option for fast and large-scale CO2 emission reduction in road freight transport. Yet, the limited range and longer charging times compared to diesel trucks make long-haul BET applications challenging, so a comprehensive fast charging network for BETs is required. However, little is known about optimal truck charging locations for long-haul trucking in Europe. Here we derive optimized truck charging networks consisting of publicly accessible locations across the continent. Based on European truck traffic flow estimates for 2030 and actual truck stop locations we construct a long-term charging network that minimizes the total number of required locations. Our approach introduces an origin-destination (OD) pair sampling method and includes local capacity constraints to compute an optimized stepwise network expansion along the highest demand routes in Europe. For an electrification target of 15% BET share in long-haul and without depot charging, our results suggest that about 91% of electric long-haul truck traffic across Europe can be enabled already with a network of 1,000 locations, while 500 locations would suffice for about 50%. We furthermore show how the coverage of OD flows scales with the number of locations and the size of the stations. Ideal locations to cover many truck trips are at highway intersections and along major European road freight corridors (TEN-T core network).

A novel method for evaluating ship concept performance in transport systems

Abstract Transport carries significant external costs such as climate change, accidents, pollution, and road congestion, driving national and international strategies for the development of new transport concepts. This includes shifting larger cargo volumes away from roads to more sustainable transport modes such as waterborne. The SEAMLESS project was launched in 2023 to answer to these needs by developing technology for autonomous waterborne zero-emission feeder-loop services. The realisation of such services depends on their modal competitiveness. Autonomous ships are expected to reduce transport costs and emissions, and ultimately improve logistical performance. There are, however, few published studies that quantify these impacts of autonomy. Furthermore, commercial waterborne autonomous transport services do not exist yet, limiting the possibilities for empirical analysis. Hence, research is needed to address exactly how and to what extent, autonomy improves competitiveness in different applications. This paper addresses the need for more empirical analyses of innovative waterborne transport performance, by presenting a novel method for evaluating ship concept performance in transport systems. The impacts of design choices are captured through hydrodynamic and logistical simulations. The method can be applied to transport systems consisting of both conventional and novel ship designs, operating on one or more routes including transhipments. It is implemented in the software SIMPACT and applied to a case study which establishes a shortsea feeder-loop service in the Bergen area in Norway. The Bergen municipality has decided that the container terminal is to be moved out of the city centre to reduce local traffic and emissions. However, in the absence of a competitive waterborne transport service in this region, the relocation will have the unfortunate consequence of an estimated annual net increase in regional truck traffic of 40,000 additional truck trips over 25km. By means of the proposed methodology, this paper investigates the feeder-loop concept and compare its quantified performance to truck transport and finds that competition is feasible.

Warehouse CITY – An open data product for evaluating warehouse land-use in Southern California

Warehouse CITY is an open data product used to visualize and quantify the cumulative impact of warehouses within Southern California. Community groups, researchers, planners, and local agencies apply this open data product in project approval processes, research, lawsuits, and education. Warehouse CITY estimates the cumulative impacts of warehouse counts, acreage, building footprint, heavy-duty truck trips, diesel particulate matter emissions, oxides of nitrogen emissions, carbon dioxide emissions, and jobs. The Warehouse CITY open data product and dashboard is available as a website and at a GitHub repository.

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.

Modeling External and Empty Freight Truck Trips using Agent-Based Simulation

Modeling external and empty freight trips helps in better planning and scheduling of transportation operations. This can lead to more effective route planning, load consolidation, and overall optimization of transportation networks. In this study external and empty truck trips modeled using Freight Analysis Framework (FAF) and Highway Performance Measurement System (HPMS) data with generating trip outputs from POLARIS agent-based simulation framework. The model then applied to the Austin metropolitan area, Texas, USA. The model estimated single and multi-unit AADT values with 91% and 93.4% accuracy compared with the HPMS volume, respectively. The estimated error – 9% and 6.6% - is due to the shortest path routing that may prefer faster routes on certain highways. Truck trips on large volume links tend to be overestimated while short links underestimate truck trips to some extent.

The potential of combining passenger rail with freight: A New York City case study

Utilizing passenger rail, including subways, to transport goods can have advantages over trucking in terms of efficiency and emissions. While some experimentation is ongoing in this area, combined passenger and rail opportunities in specific cities merit further attention. To more concretely examine how to leverage passenger rail for freight, this paper explores the potential of utilizing unused capacity in New York City (NYC) subway trains for transporting goods. Using General Transit Feed Specification (GTFS), rail and passenger data for the NYC subway, we found ways to use excess capacity and existing rail lines for freight transportation. We visualized a freight train timetable and graph, analyzed meet-errors between freight and passenger trains, and formulated prevention policies. Using U.S. Environmental Protection Agency data, we estimated emission reductions from replacing trucks with unused subway capacity. Our findings suggest that with adequate policies, investments and redesign, combining passenger rai and freight in the subways could significantly reduce truck trips, traffic congestion, and greenhouse gas emissions compared to trucking alone. However, implementing this approach requires careful collaborative planning, investment, enhanced security screening, and streamlined operations to minimize impacts on passenger transport. Further efforts should explore in more depth the costs and benefits and practical design and policy issues around using excess passenger rail capacity for freight transport in NYC and other cities.

The evolution of urban freight generation: a study of intra- and inter-city truck trips

The operations and spatial structure of logistics-related facilities has evolved with the growth of the global production network and the advancement of information and communication technology over the past several decades. Such logistics evolutions are thought to have occurred in tandem with changes in urban freight transportation systems. We estimate models for freight trip generation using the data from 2003 and 2013 in the Tokyo Metropolitan Area. The results suggest that the relationship between employment size and freight trip generation weakened during the study period and that the scale of freight generation and the generation of intercity freight trips became less connected.

Trucks Tour Chaining for Electric Demand Estimation

We propose an efficient truck trip chaining method to model freight fleet electrification using an agent-based simulation tool to simulate passenger and truck tours. The estimated truck trajectories are used to estimate vehicle energy consumption and emissions. The electricity demand estimate suggests that passenger vehicles have a significantly higher demand due to higher adoption and a larger fleet. Therefore, the truck fleet electrification efforts would not significantly affect the grid, but its adoption rates, along with passenger vehicle electrification, would cause issues.

Structure and dynamics of urban freight truck movements: A complex network theory perspective

Knowledge of the core structure and inherent dynamics of urban freight transport systems is important for the development of policies, aimed at improving the livability and sustainability of cities. The past decade has witnessed a great deal of efforts into analyzing the geographic structure of urban freight transport systems. However, in-depth studies on the system core structure and underlying dynamics are still absent. This study contributes to the field by analyzing large scale freight truck trip data from Chinese cities, using complex network analysis. We empirically reconstruct and characterize the urban freight truck mobility networks and reveal the underlying spatial interaction patterns. We develop a spatial network growth model which explains how hub-and-spoke core structure of urban freight transport systems are formed. The developed model captures the essential interaction dynamics of freight locations, and explains the effects of spatial distance, economic size and business pattern replication. Inspired by the model, we provide policy implications for land-use planning, transportation planning and sustainable urban development.

A Multi-Stage Approach for External Trucks and Yard Cranes Scheduling with CO2 Emissions Considerations in Container Terminals

Background: In container terminals, optimizing the scheduling of external trucks and yard cranes is crucial as it directly impacts the truck turnaround time, which is one of the most critical performance measures. Furthermore, proper scheduling of external trucks contributes to reducing CO2 emissions. Methods: This paper proposes a new approach based on a mixed integer programming model to schedule external trucks and yard cranes with the objective of minimizing CO2 emissions and reducing truck turnaround time, the gap between trucking companies’ preferred arrival time and appointed time, and the energy consumption of yard cranes. The proposed approach combines data analysis and operations research techniques. Specifically, it employs a K-means clustering algorithm to reduce the number of necessary truck trips for container handling. Additionally, a two-stage mathematical model is applied. The first stage employs a bi-objective mathematical model to plan the arrival of external trucks at the terminal gates. The second stage involves a mathematical model that schedules yard cranes’ movements between different yard blocks. Results: The results show that implementing this methodology in a hypothetical case study may lead to a substantial daily reduction of approximately 31% in CO2 emissions. Additionally, the results provide valuable insights into the trade-off between satisfying the trucking companies’ preferred arrival time and the total turnaround time. Conclusions: The integration of data clustering with mathematical modeling demonstrates a notable reduction in emissions, underscoring the viability of this strategy in promoting sustainability in port-related activities.

A large-scale analytical residential parcel delivery model evaluating greenhouse gas emissions, COVID-19 impact, and cargo bikes

The e-commerce industry has experienced significant growth in the past decade, particularly post-COVID. To accommodate such growth, the parcel delivery sector has also grown rapidly. However, there is a lack of study that properly evaluates its social and environmental impacts at a large scale. A model is proposed to analyze such impacts. A parcel generation process is presented to convert public data into parcel volumes and stops. A continuous approximation model is fitted to estimate the length of parcel service tours. A case study is conducted using New York City (NYC) data. The parcel generation is shown to be a valid fit. The continuous approximation model parameters have R2 values of 98% or higher. The model output is validated againstUPS truck trips. Application of the model to2021 suggestsresidential parcel deliveries contributed to 0.05% of total daily vehicle-kilometer-traveled (VKT) in NYCcorrespondingto 14.4 metric tons of carbon equivalent (MTCE) emissions per day. COVID-19 contributed to an increase in parcel deliveries that led to up to 1 064.3 MTCE of annual greenhouse gas (GHG) emissions in NYC (which could power 532 standard US households for ayear). The existing bike lane infrastructure can support the substitution of 17% of parcel deliveries by cargo bikes, which would reduce VKT by 11%. Adding 3 km of bike lanes to connect Amazon facilities can expand their cargo bike substitution benefit from a VKT reduction of 5% up to 30%. If 28 km of additional bike lanes are made, parcel delivery substitution citywide could increase from 17% to 34% via cargo bike and save an additional 2.3 MTCE per day. Cargo bike priorities can be set to reduce GHG emissions for lower-income neighborhoods including Harlem, Sunset Park, and Bushwick.

Empirical analysis and modeling of the allometric scaling of urban freight systems

Heavy trucks which undertake the majority of freight volume play an important role in urban freight systems. By analyzing heavy truck trip data, we find a superlinear scaling relationship for heavy truck trips and a sublinear scaling relationship for heavy truck numbers relative to urban population size. Although these allometric scaling relationships that widely appear in nature and social systems have been explained by many models, a simple model that can cover a wide range of scaling exponents in these systems is still lacking. Here, we develop a partially mixing city operation model by quantifying the mixability of the urban population to explain why the superlinear and sublinear scaling exponents are in the range of 1 and . This simple model not only helps us understand the mechanism of allometric scaling of urban freight systems, but also provides a new framework for other superlinear and sublinear scaling relationships in cities.

Development, Calibration, and Validation of a Large-Scale Traffic Simulation Model: Belgium Road Network

Development of large-scale traffic simulation models have always been challenging for transportation researchers. One of the essential steps in developing traffic simulation models, which needs lots of resources, is travel demand modeling. Therefore, proposing travel demand models that require less data than classical travel demand models is highly important, especially in large-scale networks. This paper first presents a travel demand model named as probabilistic travel demand model, then it reports the process of development, calibration and validation of Belgium traffic simulation model. The probabilistic travel demand model takes cities' population, distances between the cities, yearly vehicle-kilometer traveled, and yearly truck trips as inputs. The extracted origin-destination matrices are imported into the SUMO traffic simulator. Mesoscopic traffic simulation and the dynamic user equilibrium traffic assignment are used to build the base case model. This base case model is calibrated using the traffic count data. Al-so, the validation of the model is performed by comparing the real (extracted from Google Map API) and simulated travel times between the cities. The validation results ensure that the model is a superior representation of reality with a high level of accuracy. The model will be helpful for road authorities, planners, and decision-makers to test different scenarios, such as the im-pact of abnormal conditions or the impact of connected and autonomous vehicles on the Belgium road network.

Optimal Sampling Plan for Freight Demand Synthesis with Mode Choice: A Case study of Bangladesh

This paper uses a comprehensive experimental design to investigate the influence of various traffic count sampling plans for Bangladesh on the performance of the Freight Origin-Destination Synthesis model with Mode Choice (FODS-MC) developed by Kalahasthi et al. FODS-MC estimates a national-level freight demand model including trip distribution, mode choice, empty truck trips, and empty rail trips, where one of the key inputs is the freight truck and rail, traffic counts. The traffic count sample comprises three types of road links (national, regional, and zilla) and one category for the rail link across the country. A Box–Behnken Design (BBD) with a response surface for each of four FODS-MC parameters (distribution, mode choice, truck empty trips, and rail empty trips) is constructed. The results showed that the response surfaces are nonlinear planes for all parameters. There is no single optimal sampling plan for FODS-MC as each model parameter demands different distribution across the truck and rail links. The random and stratified samples perform almost similarly if less than 20% of the sample is collected. Minimizing the loss functions between the estimated and true parameters shows that a random sample between 20% and 25% of the truck and rail links estimates the best freight demand model. Overall, this research develops a framework to assist public practitioners in the optimum usage of the limited time and resources in collecting the traffic count data that could estimate the freight demand and mode choice models effectively.

Coordinating Multiple Cooperative Vehicle Trajectories on Shared Road Networks

This paper addresses the problem of planning trajectories for multiple cooperative agents along specified paths through a static road network. Vehicle trajectories are coupled through interactions at shared areas, including intersections and road segments. A motion model is presented that includes the combined trajectories of all vehicles. The trajectories are optimized for travel time by solving a Mixed Integer Linear Program (MILP) that incorporates constraints to ensure collision avoidance. To improve the computation time of solving the MILP, two model modifications are introduced related to interactions: 1) The use of binary variables is reduced by grouping them at targeted travel times. 2) The relative travel order of interacting vehicles is constrained, guided by a heuristic solver. To further reduce computation time, an iterative method solves a sequence of smaller MILPs by targeting essential vehicle interactions with an algorithm that predicts interactions. Experiments are run on a simulated road network based on a real surface mine and haul truck trips. The proposed MILP methods are shown to significantly reduce solution costs compared to a reactive approach based on common driving practices. The MILP modifications significantly reduce computation time, trading off marginal solution quality. The reduction is extended by the iterative MILP scheme without degradation of solution quality.

Research on the rational profile of exit trails of auto transport in deep open mines

Purpose. Technical and economical justification of the parameters of the profile of exit trails without horizontal sections in the operation of road transport in deep open mines. Methodology. To solve the objectives, the following methods were used: analytical, graphic methods, mathematical modeling, comparison of options, and analogies. These research methods are used when studying and justifying of a rational profile of exit trails of auto transport in the open mine, as well as defining the width of the working platforms of the excavator, and doing technical-economical calculations of the proposed solutions. Findings. Based on the performed analysis of parameters determining the distance of transportation of rock mass, it was found that when using the exit trails without horizontal sections, the transportation distance is 300 m, and with horizontal sections 531.3m, which exceeds the former by 1.77 times. The trip time when using the transportation scheme without horizontal sections is 7.44 min, and when using it with horizontal sections, it is 8.42 min. It is proved that the use of exit trails without horizontal sections in the open mine of Poltava Mining and Processing Plant reduces the cost of diesel fuel by 42.57% and capital costs by 40.5%. Originality. A comparison of the actual distance of transportation, and time of dump truck trip by component operations is carried out, and expenses for maintenance of open mine roads when using trails with horizontal sections and without them are calculated. The scheme of exit trails without horizontal sections has been offered for the first time, which allows a reduction in the distance of transportation of rock mass by dump trucks with a reduction of rock volume for carrying out conventions by 57%. Practical value. The developed scheme of the route profile without horizontal sections can be applied to overburdening operations, and the development and deepening of open mines for the extraction of different types of minerals, such as iron ore and hard coal. The expected economic effect from the introduction of trails without horizontal sections is determined by the reduction of diesel fuel costs by 52.9 UAH/t.km, and accordingly 1.68 million UAH for the conduction of conventions.

SISTEM MONITORING TRUK KELAPA SAWIT MENGGUNAKAN GPS TRACKING BERBASIS WEBSITE

Transport vehicles in the form of trucks are one of the important components in supporting the logistics system when collecting crops in a plantation company. Fresh fruit bunches (FFB) produced by oil palm plantations must be sent to the location of the crude palm oil (CPO) processing plant at a predetermined time so that fruit quality is maintained. Therefore, scheduling and arranging the route of truck trips plays an important role. Constraints faced in plantations are the absence of a cellular internet network in plantations and limited road infrastructure so that location, time and route data cannot be accurately monitored. Scheduling can be done optimally and can reduce waiting times and queues of trucks that want to enter the shelter location. To overcome this problem, a monitoring system for oil palm trucks was created using a website-based GPS tracking. The components used are GPS Neo-6M, NodeMCU ESP8266, GSM SIM800L. It can be concluded that the tool can display data and information as expected, and the tool works well and can control the coordinates of the oil palm truck remotely.

The applications of multiple route optimization heuristics and meta-heuristic algorithms to solid waste transportation: A case study in Turkey

The dense population and high solid waste production make it challenging to determine the optimal routes for garbage collection trucks in metropolitan cities. Moreover, incorrectly planned routes cause waste of fuel, time, and other resources in the municipal garbage collection process. Vehicle Routing Problem (VRP) is a combinatorial optimization problem that aims to find optimal routes for a certain number of vehicles that start from a depot, visit all customers, and return to the depot. This study aims to find the optimal routes that satisfy all the needs by considering the waste collection process of the Istanbul Umraniye district. Data on the currently used routes, daily tonnages, number of truck trips, and the number and locations of containers in the Umraniye district were obtained from the Umraniye municipality. We solve the route optimization problem of the Umraniye municipality with multiple heuristics and meta-heuristic algorithms like the nearest neighbor, savings, Christofides, simulated annealing, greedy descent, guided local search, and Tabu Search algorithms. We show the examined heuristics and meta-heuristics can produce fast and feasible solutions to large-size real-life problems.

Multimethod simulation approach for capacity design of a truck parking area in city ports

The port induced freight can cause traffic congestion problems in city ports if road freight transportation is used. The secondary congestion problem arises from the pooling of the trucks at the port gates because of the delays of the port operations. The absence of the truck parking areas inside the port causes additional truck trips between the port and auxiliary truck servicing areas around the port. For reducing the impacts of the associated problems of the port induced truck traffic, truck parking areas can be used as buffer zones between the port and city. The purpose of this study is to develop a Decision Support System (DSS) with using multimethod simulation and cost optimization model for the capacity design of a truck parking area for a city port. The preliminary design of the parking area is used to estimate the development cost and outputs of the simulation model is coupled for the capacity optimization for truck arrival scenarios. The methodology is implemented for a case study of the İzmir city port in Turkey. The results of the study indicated that significantly different parking area capacities are required for different truck dwell times for the time restricted and unrestricted truck arrivals

Applications of Passive GPS Data to Characterize the Movement of Freight Trucks—A Case Study in the Calgary Region of Canada

The movement of trucks represents a significant portion of travel. Surveys have traditionally been used to measure truck movement, but this costly and limited data collection method typically involves in-person interviews and requiring high workload. This study explores different ways in which passive truck GPS data can be used to complement traditional data collection methods, for obtaining detailed information about the travel behaviors of freight trucks. First, we develop a heuristic-based model to identify truck stops. A new methodology is proposed to classify truck stops into a primary or secondary sto, which make identifying trip purposes possible. Primary stops are defined as the locations where the loading or unloading of the goods takes place. Secondary stops are those associated with all other purposes, including refueling, and driver breaks. Finally, we develop a destination choice model for modeling truck movements. This model applies a discrete choice modeling technique to distribute truck trips within the Calgary region, Canada. We test the utility function in the destination choice model to include of business establishment data, travel impedances, and other dummy variables that are likely to influence truck demand. The results show that a combination of trucks’ dwelling times and their entropy can be used to classify truck stops by purpose. This study also shows the potential of using passive GPS data to gain additional insights into truck movements characterization and truck trip distribution modeling.