Freight Transportation Planning Research Articles

Integration of Google Maps API with mathematical modeling for solving the Real-Time VRP

Freight transportation is the backbone of urban economies and plays a critical role in the smooth functioning of cities. As such, devising efficient methods for freight transportation planning is of paramount importance. One of the most crucial aspects affecting the efficiency of freight transport is the variability in travel speeds, impacted by factors such as traffic congestion. While traditional approaches often rely on GPS technologies and associated routing services—which can be expensive—for planning, these methods also necessitate frequent re-optimization due to ever-changing traffic conditions. To address these challenges, we introduce a novel solution that integrates real-time traffic data into daily vehicle route planning. Specifically, our method incorporates Google Maps API for traffic congestion estimation and utilizes a Mixed Integer Linear Programming (MILP) model to determine optimal routes for an entire day. We tested our methodology in a major U.S. city and found that it outperforms conventional approaches by up to 18% in terms of routing time, underscoring its practical relevance and efficiency.

Examining the Effects of Local Sample Sizes on Spatial Transferability of Freight Production Models

Recent research in freight transportation planning has been exploring the spatial transferability of freight demand models, which can help the planning agencies of developing economies save the cost and time incurred in freight surveys. As the demand models are time-, cost-, and data-intensive, it is prudent to analyze the effects of sample size on the transferred model in a region. The findings and inferences from such analysis will save resources in freight data collection programs. Earlier, conventional models like ordinary least squares (OLS) regression were assessed for transferability. However, the predictive ability and transferability of such non-conventional models are not well studied. It is necessary to understand whether the extent of transferability of non-conventional models is greater than that of conventional models so that planning agencies can adopt more reliable modeling approaches. This paper investigates the spatial transferability of freight production models using OLS, robust regression, and multiple classification analysis (MCA). The results of the transferability assessment show that MCA models have better transferability using the naïve transfer method. In addition, transferability is assessed for different sample sizes to examine the variation in the extent of transferability. The MCA models have shown the least deviation, indicating that these models are preferred for transferability when the sample size is small.

Sustainable Design and Operations Management of Metro-Based Underground Logistics Systems: A Thematic Literature Review

Sustainable urban development relies on forward-looking infrastructure development. As an emerging infrastructure system that incorporates green technologies, the Metro-based Underground Logistics System (M-ULS) enables sustainable transportation of passengers and freight within cities collaboratively by sharing rail transit network facilities. M-ULS can effectively save non-renewable energy and reduce pollution to the ecological environment, and the comprehensive benefits of the system make an outstanding contribution to sustainable urban development. The purpose of this study is to provide a systematic review of M-ULS based on different perspectives and to present the development of the M-ULS network integration concept. By employing bibliometric analysis, the four dimensions of M-ULS related literature are statistically analyzed to discover the knowledge structure and research trends. Through thematic discussions, a development path for developing the concept of M-ULS network integration was established. The main findings of this study are summarized as follows: (i) A comparative analysis shows that the metro system has a high potential for freight use; (ii) Improvements in metro freight technologies are conducive to urban economy, environment, and social sustainability; (iii) Network expansion is an inevitable trend for implementing underground logistics based on the metro; (iv) The interaction among public sectors, metro operators, logistics corporations, and users plays a critical role in promoting the development of M-ULS. (v) It is worth mentioning that the planning of green infrastructure should fully consider its comprehensive contribution to the sustainable development of the city. This study visualizes the current status and hotspots of M-ULS research. It also discloses frontier knowledge and novel insights for the integrated planning and operations management of metro and urban underground freight transportation.

Percepções conflitantes dos envolvidos no planejamento do transporte urbano de mercadorias em cidades brasileiras: o caso de Palmas (TO) e São Leopoldo (RS)

The urban freight transport plays an important role in economic development. However, the negative externalities of the activity can compromise urban mobility, and therefore, city planning needs to intervene with solutions to minimize these externalities. However, the assertiveness of the solutions is directly linked to the participation of the stakeholders involved in the activity. In this paper is compared the perceptions of carriers, retailers and public authorities about problems and solutions for the urban freight transport in two cities of similar size, but with different levels of regional polarization. The results indicated few parameters of agreement between the stakeholders of each city, confirming that they have conflicting objectives that end up generating different perceptions and, consequently, divergence in the solutions for the urban freight transport. This scenario reinforces the need for dialogue between those involved so that solutions can encompass the vision of all stakeholders, in search of solutions converging to the identified objectives and problems. In addition, the differences observed between the context of each city reinforce the complexity of urban freight transportation planning and formulating public policies that meet the needs of each municipality according to their needs.

Exploring the contextual factors behind various phases in logistics sprawl: The case of Seoul Metropolitan Area, South Korea

Logistics sprawl is a phenomenon that allows retailers and distributors to cope with the increasing demand for processing large freight volumes through the globalized production and distribution systems. It involves a tradeoff between per-unit inventory and transport costs. Large-scale, automated facilities are built on the urban periphery, where relatively cheaper land is more available. A potential increase in shipment distance necessitates better freight transport management and planning by logistics businesses and the public sector. At present, online shopping sales are skyrocketing, and consumers increasingly demand instant delivery at no cost. Online retailers strive to fulfill the demand in urban settings, characterized by high land rent, congestion, and strict regulations. Building an urban logistics system can become extremely costly and time-consuming. Public interventions may be justified if urban logistics is considered an essential utility that can lead to significant negative externalities without proper management. This research navigates how and where warehouse development has occurred under such stringent circumstances. I first examine the spatial dynamics of warehouse development over the last three decades in the Seoul Metropolitan Area, South Korea, and attempt to expand the conventional understanding of logistics sprawl. Relative distribution is calculated between warehouses and potential destinations: B2C consumer (population), B2B I (employment), B2B II (industrial park), and Seoul's central business district. The primary data source is the Korea National Building Registry, which contains 3340 warehouse development records. Four phases in the spatial dynamics of warehouse development are documented: stagnation (1991–1998), sprawl (1998–2007), peak (2007–2008), and return (2009–present). Further, semi-structured interviews with field experts identify the business, economic, and regulatory circumstances that potentially influence logistics facility development. The results suggest four factors behind logistics sprawl: (1) logistics restructuring driven by a consumption-based economy, (2) location factor tradeoffs, (3) disruptive economic shocks, and (4) regulatory reform. This research calls for comprehensive land use and transport planning to better accommodate urban logistics practices.

Model of operational planning of freight transportation by tram as part of a green logistics system

The introduction of environmentally friendly technologies is becoming increasingly necessary to combat global warm-ing and air pollution in cities. The concept of eco-logistics is seen as an effective approach to the management of materials and related flows in order to reduce environmental and economic damage to the environment. The sustaina-ble development of green supply chains is based on the use of environmentally friendly types of vehicles, reduction of energy and other resources consumption, optimization of transport and technological processes in delivery systems. As part of the development of green supply chain, it is proposed to transport goods by freight trams, which eliminates the need for heavy trucks in the city, improves traffic conditions and reduces the environmental impact of transport. The research was conducted for the city of Poznan. The distribution system of the city of Poznan operates in conditions of stochastic demand for deliveries from clients and the risk of lack of sufficient supplies in distribution centers. To take into account the specificity of the distribution system of cargo delivery in conditions of uncertainty and risk, a simula-tion model of the organization of the material flows within the transport system of the city of Poznan has been pro-posed. The result of simulation is the optimal assignment of clients to the distribution centers, as well as the value of total mileage with the load, which is a random variable. It is assumed that the random variable is distributed according to the normal distribution law. The results were calculated and compared for two variants, i.e. for constant demand and sufficient quantity of cargo in distribution centers, and for variable demand and uncertainty conditions, e.g. insuffi-cient cargo quantity in distribution centers. The purpose of the paper is to develop a simulation model for planning supplies of small consignments of goods by trams implementing green logistics concept with variable demand for transportation. After a short introduction of the problem, the literature review related to the concept of green logistics and requirements of transport and distribution system are presented in section 2. In section 3, the research problem and research methodology are described. Section 4 provides the results of assignment of clients to distribution centers. The paper ends with concluding remarks.

A New Model for Evaluation of the Passenger and Freight Transportation Planning Based on the Sustainability and Safety Dimensions: A Case Study

A New Model for Evaluation of the Passenger and Freight Transportation Planning Based on the Sustainability and Safety Dimensions: A Case Study

Modelling Freight Trip Generation Based on Deliveries for Brazilian Municipalities

Freight trip generation modelling is important for forecasting freight movements, and understanding freight movements is crucial to enabling sustainable freight transportation planning. The existing literature focuses on model development, and most of the previous models are estimated by ordinary least squares regression. However, few studies have carefully considered the OLS assumptions. The objective of this paper is to estimate freight trip generation models using deliveries to commercial establishments in Brazilian municipalities. A procedure is described to estimate models by ordinary least squares (OLS), and alternative techniques are considered to address the violations of the OLS assumptions. The analysis was conducted with data from 860 commercial establishments in nine Brazilian municipalities, and models were estimated for capital, non-capital, small, medium, and larger municipalities. The findings showed that alternative techniques to OLS regression can provide better-estimated parameters and more accurate results. Not evaluating the OLS assumptions could compromise the quality of the model and, consequently, planning using these models. Moreover, the results showed that the number of employees has a more significant influence in small cities and a lower influence in medium-sized municipalities. Finally, the findings demonstrated the importance of local models that include the municipalities’ characteristics and that can support freight transportation planning. These models can also include sustainable strategies for freight transport.

Examining commercial vehicle fleet ownership decisions and the mediating role of freight generation: A structural equation modeling assessment

Commercial Vehicle (CV) ownership is a variable of interest in planning interventions and policy instruments for sustainable transportation, but the decision-making process to own a vehicle fleet is not well understood in the context of freight transportation. The necessity to understand the heterogeneity in CV type ownership arises from the need to mitigate externalities associated with them. We argue that the need to own CVs is a derived demand arising out of the demand to move goods and is influenced by freight handling requirements of businesses. To this end, this paper examines the dual role (mediation) of freight generation variables while modelling CV ownership, which is an underexplored area of research in freight transportation planning. Establishment-based freight survey data from seven cities in Kerala, India, are used to develop structural equation models. The CV ownership is analysed through freight generation variables as mediators, apart from the direct influence of business size indicators and commodity characteristics. The study findings confirm the intermediatory role of freight generation and show the consequences of neglecting such a dual role in the overall CV ownership modelling paradigm. The model estimates offer insights into the causal structure of the decision-making process of establishments to own different levels of fleet ownership and composition. Understanding the business managers’ decisions to own CVs in their fleet can be used for freight traffic management using urban consolidation centre (UCC). The statistical findings also provide the total effects between fleet and employment size and how freight generation mediates this relationship. The path diagrams representing the CV ownership patterns can be useful to policy makers while implementing policies like low-emission zones.

Multiperspective Analysis of Pandemic Impacts on U.S. Import Trade: What Happened, and Why?

U.S. container ports have experienced unpresented congestion since mid-2020. The congestion is generally attributed to import surges triggered by heavy spending on consumer goods during the COVID-19 pandemic. Port congestion has been compounded by the inability of importers to retrieve, receive, and process all the inbound goods they have ordered, resulting in supply chain shortfalls and economic disruption. How can the shipping industry and government organizations predict the end of the current surge and anticipate future surges? Expected seasonal variations in import volume are associated with peak holiday shopping periods; nonseasonal import surges are signaled by other factors. The research goes beyond transportation data sources to examine broader connections between import volume and indicators of economic and retail industry conditions. The strongest and most useful relationship appears to be between retail inventory indicators and containerized import growth. From January 2018 through July 2021, there was a relatively strong negative correlation between retail inventory- and import TEU indices with a 4-month lag (corresponding roughly to the time between import orders and -arrival). In the 2020 to 2021 pandemic period the negative correlation was stronger, again with a 4-month lag. These findings suggest that observers might anticipate import surges after marked, nonseasonal drops in retail inventories, and that import surges are likely to last until target inventory levels are restored. In a broader sense, an awareness of the linkages between consumer demand, retail chain responses, and containerized import volumes could better inform port, freight transportation, and government planning and policy choices.

Hybrid modeling of collaborative freight transportation planning using agent-based simulation, auction-based mechanisms, and optimization

The sharing economy is a peer-to-peer economic model characterized by people and organizations sharing resources. With the emergence of such economies, an increasing number of logistics providers seek to collaborate and derive benefit from the resultant economic efficiencies, sustainable operations, and network resilience. This study investigates the potential for collaborative planning enabled through a Physical Internet-enabled logistics system in an urban area that acts as a freight transport hub with several e-commerce warehouses. Our collaborative freight transportation planning approach is realized through a three-layer structured hybrid model that includes agent-based simulation, auction mechanism, and optimization. A multi-agent model simulates a complex transportation network, an auction mechanism facilitates allocating transport services to freight requests, and a simulation–optimization technique is used to analyze strategic transportation planning under different objectives. Furthermore, sensitivity analyses and Pareto efficiency experiments are conducted to draw insights regarding the effect of parameter settings and multi-objectives. The computational results demonstrate the efficacy of our developed model and solution approach, tested on a real urban freight transportation network in a major US city.

Multi-Objective Path-Decision Model of Multimodal Transport Considering Uncertain Conditions and Carbon Emission Policies

To develop low-carbon transport and promote sustainable economic development, this paper took the uncertainty in highway transport speed and transshipment time into account in the actual transport process and established multi-objective path-decision models of multimodal transport under different carbon policies. The expectation values of nonlinear uncertainties were estimated by Law of Large Numbers (LLN), and the models were solved by the K-shortest paths algorithm and non-dominated sorting algorithm (NSGA-II), whose advancement and effectiveness were verified through the comparison of SPEA2. Based on the Pareto theory, the optimally symmetrical compromise between the objectives and the influence of the transport speed uncertainty and carbon emission policies on path decisions were quantified and discussed. Taking the multimodal transport network of West Africa as the experimental background, the practicability of the path-decision results is analyzed, and a trade-off analysis is also conducted to provide the theoretical foundation for future freight transport planning.

Inland waterway network mapping of AIS data for freight transportation planning

AbstractTravel demand models (TDMs) with freight forecasts estimate performance metrics for competing infrastructure investments and potential policy changes. Unfortunately, freight TDMs fail to represent non-truck modes with levels of detail adequate for multi-modal infrastructure and policy evaluation. Recent expansions in the availability of maritime movement data, i.e. Automatic Identification System (AIS), make it possible to expand and improve representation of maritime modes within freight TDMs. AIS may be used to track vessel locations as timestamped latitude–longitude points. For estimation, calibration and validation of freight TDMs, this work identifies vessel trips by applying network mapping (map-matching) heuristics to AIS data. The automated methods are evaluated on a 747-mile inland waterway network, with AIS data representing 88% of vessel activity. Inspection of 3820 AIS trajectories was used to train the heuristic parameters including stop time, duration and location. Validation shows 84⋅0% accuracy in detecting stops at ports and 83⋅5% accuracy in identifying trips crossing locks. The resulting map-matched vessel trips may be applied to generate origin–destination matrices, calculate time impedances, etc. The proposed methods are transferable to waterways or maritime port systems, as AIS continues to grow.

An integrated optimization approach using a collaborative strategy for sustainable cities freight transportation: A Case study

• Logistics collaboration is studied in the planning of sustainable city freight transportation. • An integrated and collaborative approach is proposed for sustainable freight logistics networks. • Three sustainability dimensions are considered. • A case study is conducted to show the applicability of proposed approach and the effects of collaboration on the sustainable development of the society and the environment. In recent years, the increasing freight transportation activities has caused extensive economic, environmental and social impacts on society. Therefore, it becomes necessary for logistics companies to find high-performance strategies to enhance their sustainability. Collaboration between companies appears as one of the promising solution that improves the freight transport efficiency of modern cities. Existing studies on collaborative transport did not sufficiently account for sustainability aspects and they mainly discuss the independent planning of decision-making problems with economic objectives. However, collaboration an integrated decision-making process can significantly ameliorate the effectiveness of urban transportation systems. This study addresses the issue of sustainable freight transportation planning by formulating and solving a collaborative and integrated two-echelon inventory, location and routing problem (2E-CILRP). The 2E-CILRP is formulated as a multi-objective integer programming model that minimizes logistics costs, C O 2 emissions and accident rate. A hybrid heuristic based on machine learning is also introduced to solve the studied problem. The proposed approach combines k -means clustering and genetic algorithms. Comparison of the obtained results with those provided with exact method reveals that the developed approach is more efficient and more practical for designing integrated and collaborative networks with large-sized data. A case study of four agri-food SMEs in France is investigated in order to demonstrate the sustainable benefits of the developed optimization approach. The obtained results suggest that the developed approach can significantly reduce the level of C O 2 emissions, logistics costs and accident rate caused by transportation in cities.

Information on Resource Utilisation for Operational Planning in Port Hinterland Transport

To meet increased freight flows through maritime ports, a high level of resource utilisation in hinterland transport is of crucial importance. However, various perspectives on resource utilisation create issues with use of information for operational decisions in port hinterland. The purpose of this paper is to explore the use of information related to resource utilisation for operational planning in port hinterland freight transport to facilitate its improvement. The study is case-based, and the data is collected through semi-structured interviews, visual observations, and company documents. The findings are analysed with a framework built from literature emphasising different resource utilisation perspectives and the use of information in road freight transport chain decisions. The findings show that the use of information on resource utilisation in operational freight transport decisions in the port hinterland transport system is limited and lacks a complete system overview. Instead of the information on measured parameters, different types of estimates of efficiency parameters (including resource utilisation) are commonly used for operational planning decisions. The information about the measured indicators has to be combined with other information to obtain an efficient level of resource utilisation; otherwise, it could generate incorrect assumptions regarding utilisation. The paper contributes to the topic of operational freight transport planning by describing the use of information on resource utilisation.

Understanding urban logistics and consumer behavior in São Paulo city

Making deliveries of Fast-Moving Consumer Goods (FMCG) to small retailers in dense areas is a challenge. This has different levels of impact between high-income and low-income neighborhoods. They are more evident in megacities in developing countries, where there is a clear division between classes. On the one hand, governments do not attend low-income neighborhoods where there are many unsafe areas, lack of planning, and infrastructure that hinders FMCG deliveries. On the other hand, traffic regulations in high-income neighborhoods make delivery processes more complex. This paper aims to analyze urban logistics, comparing low- and high-income areas in São Paulo city taking into account local markets, consumer buying behavior, and the distribution process. To achieve this, a questionnaire in loco was applied to the stakeholders involved (consumers, small retailers, and carriers). The data were analyzed by using descriptive statistics. The results from small retailers, consumers, and carriers reveal that in their point of view the delivery of products is different between Base of the Pyramid (BOP) and high-income consumers (HIC). Factors such as ‘time window regulation’ and ‘infrastructure of neighborhoods’ hinder the delivery of products from companies to small retailers in regions of HIC, while in BOP regions small retailers' security and infrastructure are the major problems from the point of view of the actors. Consumer behavior poses an important factor to guide urban freight transport planning in both regions.

Applying the Maximum Entropy Model to Urban Freight Transportation Planning: An Exploratory Analysis of Warehouse Location in the Belo Horizonte Metropolitan Region

In Brazil, neglect and lack of data have complicated urban freight transportation planning. Belo Horizonte is one such example, where the administration’s disregard for urban freight transport has led to multiple negative externalities. Using the maximum entropy method, this study analyzed the influence of urban planning factors on warehouse location in the Belo Horizonte Metropolitan Region. Warehouse location was analyzed using 19 factors, with the results recorded on a map of logistic probabilities concerning locating warehouses. Given this is exploratory research, the model was validated by visual comparison to address the four hypotheses: (i) warehouses tend to be located near roads and railroads; (ii) warehouses tend to be located close to their consumer markets; (iii) warehouses tend to be located in urban areas; and (iv) warehouses tend to be located next to each other, creating clusters. These hypotheses were based on the extant literature and confirmed from observations of the Belo Horizonte Metropolitan Region. The results also demonstrated that the maximum entropy method can suitably model freight transportation in support of urban planning policy.

Freight transportation planning in platform service supply chain considering carbon emissions

The online-to-offline (O2O) platform is becoming increasingly popular in today's market. This study investigates the decision-making problem of an O2O freight platform facing uncertain demand and carbon emission constraints to achieve sustainable development and establish an environment-friendly image. By receiving customer orders online and then delivering them offline, the platform chooses the optimal dispatching time and pricing level to maximize its profit under a limited carbon cap. We consider a stochastic model with two kinds of demand functions, i.e., additive and multiplicative cases, and solve the optimization problems. By adopting a data set from a leading O2O freight platform in China, we find that the proposed model can effectively increase the revenue of the platform by more than 20 % if the platform increases the price appropriately. The results further show that market parameters such as potential market size and price elasticity have a more significant influence on the price decision than logistics parameters such as the fixed shipping cost and holding cost per unit volume per unit time, while logistics parameters affect the dispatching time decision more significantly than market parameters. With respect to the profit, as the carbon cap increases, the profit of the platform first climbs up to a peak point and then decreases with the further increase of the cap due to rising inventory holding costs. Furthermore, the O2O platform can benefit from a large-scale market and thus might suffer a loss at its start-up stage. Our study contributes to the literature on the O2O platform and the freight transportation planning with the consideration of sustainable development.

Analysis of Intra-Urban Freight Traffic Pattern in Lagos State, Nigeria

This study examined the intra-urban freight trips pattern in Lagos State, Nigeria. Major traffic bottlenecks along selected roads served as data collection stations for this study. At each station, data were collected on type of vehicle, origin of trip, destination of trip, land use at origin and land use at destination by trained field assistants. A combination of observatory survey and questionnaire administration to freight vehicle drivers was used for the data collection. A total of 12,533 drivers of freight vehicles who at the time of the survey were conveying goods in their vehicles for delivery were randomly sampled. Both descriptive and inferential statistical techniques were employed for data analysis. Simple percentages, averages, tabulation and charts are some of the descriptive statistical techniques employed to measure the spread of observations. Two-Way Analysis of Variance (ANOVA) was used to test the hypothesis that “freight vehicle’s trips do not vary by land use types in the study area”. Urban land use was found to significantly influence freight trips. The study revealed that commercial, industrial, residential and circulatory land uses are the pivots upon which urban freight movement revolves in the study area. Commercial land use is the largest generator (42%) and attractor (34%) of urban freight trips. Industrial land use generates 28% and attracts 20% of the urban freight trips, while residential land use generates 4% and attracts 26%. Also, circulatory land use generates 17% and attracts 8% of the urban freight trips. The analysis also revealed that small trucks are the commonly used vehicles for intra-urban freight movement in the study area accounting for about 47% of the freight trips. Also, freight vehicles’ trips significantly varied by land use types, F (4, 1) = 3097.39, P = 0.13. This study concludes that land use types played a significant role in the pattern of freight traffic distribution in the study area. It was therefore recommended that efforts should be directed towards effective freight transport planning and management in the study area.

A multi-objective genetic algorithm approach to design optimal zoning systems for freight transportation planning

This paper contributes to the existing research on freight transportation, spatial and land use planning by investigating an improved spatial aggregation technique to delineate desirable freight traffic analysis zones. Zoning is a process of spatially aggregating several predefined basic spatial units (BSUs) into multiple zones. It plays a vital role in the transportation planning and decision-making process and is well-documented as the modifiable areal unit problem (MAUP). MAUP involves aggregating BSUs to obtain optimal zones satisfying specific criteria and constraints. This paper proposes an improved spatial aggregation methodology to develop a freight traffic analysis zone system by applying the multiobjective optimization concept using a genetic algorithm. The decision variables, namely, (i) Freight trip density; (ii) Number of establishments; (iii) Employment density; and (iv) Compactness, are chosen to represent the elements of freight, passenger traffic, and land use. The problem is formulated as a multiobjective network partitioning problem. The four objectives aim to create zones with better homogeneity and compactness. It is solved using a genetic algorithm with a weighted distance metric approach to prioritize the four objectives. Results show that zones resulting from the improved methodology are superior to the existing zones in terms of homogeneity of decision variables and compactness. The findings are expected to help the decision-making process of urban, transportation, and land-use planners in selecting appropriate freight traffic zone delineation strategies for a given region.