Multimodal Transportation Planning Research Articles

Multimodal Transportation Planning for Intercity Travel Between Alexandria and Borg Al Arab Cities

Multimodal Transportation Planning for Intercity Travel Between Alexandria and Borg Al Arab Cities

Synchromodal Supply Chains for Fast-Moving Consumer Goods

Synchromodality is an emerging concept in supply chain management. A synchromodal supply chain can be defined as a multimodal transportation planning system, wherein the different agents work in an integrated and flexible way that enables them to dynamically adapt the transport mode based on real-time information from stakeholders, customers, and the logistic network. The potential of synchromodality for the fast-moving consumer goods (FMCG) industry is related to the nature of business. The FMCG market is characterized by relatively low margins and high turnover, which is especially important in export supply chains. However, for a company, it may be challenging to objectively evaluate the costs and benefits, not to mention the design of a synchronized supply chain. In order to facilitate the adoption of the concept and guide the practitioners, our study put forward the following research questions: What should be considered in incorporating synchromodality in the export supply chain for FMCG? How should companies approach tradeoffs among factors affecting the supply chain? To answer these questions, we propose an adaptable framework, which should be considered a primary contribution of our study. The framework incorporates the center of gravity model, mixed integer linear programming, and sensitivity analysis. The framework is validated using a real-world problem from a multinational FMCG company. The problem involves the optimal volume allocation and the selection of the most efficient transportation mode for inland freight. Our study demonstrates that incorporating synchromodality in the export supply chain could reduce the overall cost by 9% and enhance flexibility by allowing multiple modes of transportation.

Multicriteria Feasibility Evaluation for Rural Transit in Georgia

This article describes transit feasibility analysis features of the Georgia Department of Transportation’s (GDOT’s) Multimodal Transportation Planning Tool (MTPT). Using open databases that are available agencywide, the MTPT can provide a system-level analysis of transportation requirements of rural areas, identify potential implementation constraints early in the planning process, and develop a prioritized project list by mode for an analysis region. In addition to demand-responsive transit, the MTPT addresses intercity bus, intercity passenger rail, commuter rail, highways, aviation, and bicycle modes. This article focuses on the demand-responsive transit component of the MTPT, and provides an overview of how the MTPT can help automate system-level transit planning for the general public in rural Georgia.The MTPT transit analysis assesses service feasibility for nonurbanized areas that do not currently have local transit service for the general public. In the MTPT, transit service feasibility considers the existence of human service transit providers in the county; the percentage of population in certain “target” populations; and estimates of potential ridership, vehicle requirements, capital costs, operating costs, and economic benefits. The first factor indicates if opportunities may exist to coordinate service with existing providers, and helps to address funding issues. The second factor assesses market characteristics of the target area as one determinant of potential service need. The third factor provides a range of potential values for key operating and financial statistics at a system-planning level of analysis. The intent of the analysis is to identify those locations where it makes sense to more seriously analyze new demand-responsive services.This article should be helpful for transportation planners with responsibility for developing system-level transportation plans and programs at the county, region, and state levels. It will also be helpful for decision-makers who are trying to match transit funding to areas with high service needs.

Optimal Fixed Route for Multimodal Transportation of Vehicle Logistics in Context of Soft Time Windows

In order to improve customer satisfaction and reduce the cost of vehicle logistics transportation, this paper adopts the mixed-integer programming model to analyze the delivery routes of vehicle logistics and make simulation and analysis based on the real delivery case. The results show that, compared with a single transportation scheme, the vehicle logistics optimization scheme based on the mixed-integer programming model is able to produce the optimal multimodal transportation plan, which can reduce the transportation costs, improve the service of transportation enterprises, and enhance their core competitiveness.

Research on the Choice of Multimodal Transportation Emission Reduction Schemes Based on Different Carbon Tax Models

Different carbon tax collection methods have an important impact on the emission reduction effect of multimodal transportation. In the entire chain transportation process of containerized cargo multimodal transportation, the generalized external cost is considered to establish the total utility model of multimodal transportation. Based on the optimal carbon tax rate, we studied the impact of single tax rate and progressive tax rate on carbon emissions of multimodal transportation schemes. Also, the Tianjin-Weihai section of the Bohai Rim Economic Circle is used for example analysis. The results of the study found that under different carbon tax starting points, the cost of electronic goods can be reduced by 80-200 yuan per ton, and the carbon emissions can be reduced by 350-600 tons. The effect of reducing emissions is also different for different cargo values. By adjusting the structure of cargo transportation, an optimal multimodal transportation plan considering carbon tax is proposed to provide a basis for carbon tax policy formulation and implementation.

A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

This paper compares different solution approaches for the multi-objective shortest path problem (MSPP) on multigraphs. Multigraphs as a modelling tool are able to capture different available trade-offs between objectives for a given section of a route. For this reason, they are increasingly popular in modelling transportation problems with multiple conflicting objectives (e.g., travel time and fuel consumption), such as time-dependent vehicle routing, multi-modal transportation planning, energy-efficient driving, and airport operations. The multigraph MSPP is more complex than the NP-hard simple graph MSPP. Therefore, approximate solution methods are often needed to find a good approximation of the true Pareto front in a given time budget. Evolutionary algorithms have been successfully applied for the simple graph MSPP. However, there has been limited investigation of their applications to the multigraph MSPP. Here, we extend the most popular genetic representations to the multigraph case and compare the achieved solution qualities. Two heuristic initialisation methods are also considered to improve the convergence properties of the algorithms. The comparison is based on a diverse set of problem instances, including both bi-objective and triple objective problems. We found that the metaheuristic approach with heuristic initialisation provides good solutions in shorter running times compared to an exact algorithm. The representations were all found to be competitive. The results are encouraging for future application to the time-constrained multigraph MSPP.

Pedestrian Traffic Signal Data Accurately Estimates Pedestrian Crossing Volumes

Existing methods of pedestrian travel monitoring are generally inefficient for collecting pedestrian data in many locations over long time periods. In this study, we demonstrate the validity of using a novel and relatively ubiquitous big data source—pedestrian data from high-resolution traffic signal controller logs—as a way of estimating pedestrian crossing volumes. Every time a person presses a pedestrian push button or a pedestrian call is registered at a signal, this information can be logged and archived. To validate these pedestrian signal data against observed pedestrian counts, we recorded over 10,000 h of video at 90 signalized intersections in Utah, and counted around 175,000 people walking. For each hour and crossing, we compared these observed counts to measures of pedestrian activity calculated from traffic signal data, using a set of five simple piecewise linear and quadratic regression models. Overall, our results show that traffic signal data can be successfully used to estimate pedestrian crossing volumes with good accuracy: model-predicted volumes were strongly correlated (0.84) with observed volumes and had a low mean absolute error (3.0). We also demonstrate how our models can be used to estimate annual average daily pedestrian volumes at signalized intersections and identify high pedestrian volume locations. Transportation agencies can use pedestrian signal data to help improve pedestrian travel monitoring, multimodal transportation planning, traffic safety analyses, and health impact assessments.

Assessing the Impacts of Weather on Pedestrian Signal Activity at 49 Signalized Intersections in Northern Utah

A deeper understanding of how weather variables affect pedestrian volumes is important, as active travelers are an essential part of a sustainable transportation system. Pedestrian data are limited for investigating the impacts of weather on walking levels, with most studies having data at only a couple of locations. Pedestrian actuation data (from push-buttons at traffic signals) overcomes this limitation. The Utah Department of Transportation archives pedestrian push-button press data for use in its Automated Traffic Signal Performance Measures system. In this study, pedestrian actuation data was used as a proxy for walking activity and weather data was collected from the National Oceanic and Atmosphere Administration. Using 15 months of daily time series data in Cache County, the impacts of weather on pedestrian signal activity were examined at 49 signalized intersections, using a log-linear time series regression analysis with categorical step-wise weather variables. The findings revealed that snow depth had the most frequent negative effect on walking activity. Snowfall (> 0.6 in.) also tended to have negative impacts when significant. Very hot maximum temperatures (≥ 90°F) were associated with lower pedestrian activity at around one-third of signals. Very low minimum temperatures (< 20°F) were also associated with lower pedestrian activity. Precipitation had a negative effect on walking levels, but at only a few signals. The study’s key findings offer implications for multimodal transportation planning (winter maintenance, shade trees, etc.) and traffic signal operations.

Synthesizing the Evolution of Multimodal Transportation Planning milestones in Indian Cities

Abstract Rapid urbanization and accelerated economic development are witnessed in Indian cities, causing city dwellers’ auto- dependency on private modes of transport and has raised questions over the sustainability of transport infrastructure in cities. Traditionally, India’s urban transport infrastructure was developed with the concept of moving vehicles rather than people.” Thus, the public transport system is inadequate and underdeveloped in most of the Indian cities. However, several initiatives were taken by the Indian government to promote sustainability and transit services were introduced into cities. The newly introduced formal modes of public transport lack integration with the previously existing transport mode at the operational, institutional, and informational level. Also, the physical and fare integration between the modes are missing in most of the Indian cities. With this motivation, the present research tries to understand the evolution of multimodal transport planning in Indian cities. For the present study, 49 planning documents were collected. An attempt was made to analyse the metadata and content of the planning documents dedicated to Multimodal transport planning. The main aim was to assess how multimodal transport has been considered while planning the urban transport infrastructure. The challenges for integrated multimodal transport systems were identified, and appropriate conclusions were derived based on the study.

Incorporating Multi-Source Urban Data for Personalized and Context-Aware Multi-Modal Transportation Recommendation

Transportation recommendation is one important map service in navigation applications. Previous transportation recommendation solutions fail to deliver satisfactory user experience because their recommendations only consider routes in one transportation mode (uni-modal, e.g., taxi, bus, cycle) and largely overlook situational context. In this work, we propose <inline-formula><tex-math notation="LaTeX">$\mathsf {Hydra}$</tex-math></inline-formula> , a multi-task deep learning based recommendation system that offers multi-modal transportation planning and is adaptive to various situational context (e.g., nearby point-of-interest (POI) distribution and weather). We leverage the availability of existing routing engines and big urban data, and design a novel two-level framework that integrates uni-modal and multi-modal (e.g., taxi-bus, bus-cycle) routes as well as heterogeneous urban data for intelligent multi-modal transportation recommendation. In addition to urban context features constructed from multi-source urban data, we learn the latent representations of users, origin-destination (OD) pairs and transportation modes based on user implicit feedbacks, which captures the collaborative transportation mode preferences of users and OD pairs. Moreover, we propose two models to recommend the proper route among various uni-modal and multi-modal transportation routes: (1) a light-weight gradient boosting decision tree (GBDT) based recommendation model; and (2) a multi-task wide and deep learning (MTWDL) based recommendation model. We also optimize the framework to support real-time, large-scale route query and recommendation. We deploy <inline-formula><tex-math notation="LaTeX">$\mathsf {Hydra}$</tex-math></inline-formula> on Baidu Maps, <xref ref-type="fn" rid="fn1">¹</xref> <fn id="fn1"><label>1.</label> <uri>https://maps.baidu.com/</uri>. </fn> one of the world's largest map services. Real-world urban-scale experiments demonstrate the effectiveness and efficiency of our proposed system. Since its deployment in August 2018, <inline-formula><tex-math notation="LaTeX">$\mathsf {Hydra}$</tex-math></inline-formula> has answered over a hundred million route recommendation queries made by over ten million distinct users. The GBDT based model and MTWDL based model achieve 82.8 and 96.6 percent relative improvement of user click ratio, respectively.

Traffic assignment model for combined mode with travel condition constraints

Combined-mode traffic assignment is one of the key links for multi-modal transportation planning. In order to quantitatively evaluate the implementation effect of combined-mode transportation and explore its assignment mechanism, a combined-mode traffic assignment model with travel constraints is proposed. First, the practical factors of combined mode in real world are discussed and integrated into the analysis of multi-modal network transformation as related constraints. Second, the physical transportation network is translated to equivalent super network (SN) and state-augmented network (SAN) based on the graph theory. Moreover, the network size constrained by the actual conditions is conducive to further analysis of combined-mode trips. Third, a tri-level combined-mode traffic assignment model is formulated based on the simulation platform of SAN. The first level of the model is to address the combined-mode choice, the second level is to transfer choice on SAN, and the third level is to characterize auto travelers’ route choice behavior. By analyzing the impedance in multi-level network, an MSA algorithm solving Nest-Logit model is proposed. Finally, numerical example is performed to validate the model. The method proposed in this paper considers the travel similarity, reduces the network scale, conforms to the travel logic and makes up for the shortcomings of traditional traffic allocation methods.

A Fuzzy Multi-Objective Routing Model for Managing Hazardous Materials Door-to-Door Transportation in the Road-Rail Multimodal Network With Uncertain Demand and Improved Service Level

A routing optimization with risk control and reduction provides a quantitative support for an efficient organization of the hazardous materials transportation where road-rail multimodal transportation plays an important role. Oriented on the viewpoint of the multimodal operator, this study investigates a hazardous materials routing problem for their door-to-door transportation through a road-rail multimodal hub-and-spoke network. It is difficult to determine the exact demand of hazardous materials for each transportation order during the advanced routing decision making. Therefore, demand uncertainty is introduced into the problem, in which the uncertain demands are modeled by triangular fuzzy numbers. To enhance the quality of service of the transportation, the hazardous materials routing optimization in this study uses fuzzy soft time windows to optimize the last-mile delivery of hazardous materials that determines the timeliness of transportation. Accordingly, this study proposes a fuzzy multi-objective routing model that is linear and considers the constraints of the customers' preferred service levels. To solve the proposed model, its crisp reformulation is first achieved by using the fuzzy expected value method and Jimenez's fuzzy ranking method. Then, the ε-constraint method is adopted by this study to obtain the Pareto frontier for the multi-objective optimization problem. Finally, an experimental case study is carried out to verify the proposed modeling of this study. By using sensitivity analysis and fuzzy simulation, the conflicting relationship among the economic, risk, reliability and timeliness objectives that improving one objective worsens the others is clearly indicated, and the insights that helps decision makers to make effective tradeoffs among these objectives to get a most suitable hazardous materials road-rail multimodal transportation plan are also summarized.

Evaluation of operational and environmental sustainability tradeoffs in multimodal freight transportation planning

Transportation planners often need to justify transportation related investments to public officials. Besides efficiency measures, the environment protection and sustainability are gaining more and more importance. In order to improve freight flow efficiency in a sustainable way, it is necessary to have a systematic tool to study the freight flow over all three major surface modes and their connections and, in turn, to help public officials identify the best way to improve freight transportation. We built a system-level intermodal simulation model that includes highways, railways, and waterways because all three modes, working together, play significant roles in freight flows. We evaluated the performance of the Louisiana freight network under different scenarios and measured the benefits of mode changes evaluating the tradeoff between environmental goals and other performance measures.

Examining Multimodal Transportation in El Paso Region: Results of the Regional Transportation Survey

Background Policy makers are placing an increased emphasis on reducing automobile dependency and developing transportation systems that are multimodal. To help develop strategies to increase the use of alternative modes of transportation, the Texas A&M Transportation Institute (TTI) worked with the El Paso Metropolitan Planning Organization (MPO) on a research project to develop methodologies, data, analysis, and tools that can be integrated into a regional multimodal transportation plan. This paper summarizes a component of this study that included a regional population survey. The El Paso transportation survey contributes to the field by integrating various elements from transportation research, behavioral and social sciences and public health. Methods The survey was implemented between September and October 2015, and the study area included all of El Paso County, Texas, and parts of Dona Ana and Otero Counties, New Mexico. An address-based sample was used to get a representative sample of individuals age 18+ in the metropolitan area. Several analyses (e.g. bivariate descriptive analysis and spatial analysis) were conducted to examine the behaviors or motivators for using or not using the various modes of transportation, with a particular focus on alternative modes. Results The results indicated the important role of understanding regional and population characteristics, as well as behavioral barriers and motivators, as an important first step in designing effective alternative travel programs and interventions. Several factors, including neighborhood environment variables, personal and social characteristics, and attitudes and habits work together to influence residents’ participation in alternative transportation. While the personal automobile is the current dominant travel mode in El Paso, residents see great value in extending public transportation infrastructure in the future. Improving connectivity, safety and the neighborhood environment were found to be particularly essential to encourage walking and bicycling in the region. Conclusions This study was one of the first known regional transportation surveys in the United States to incorporate several elements of health and well-being. Because costs and political constraints can derail physical interventions, policy interventions aimed at addressing personal and social barriers to alternative transportation modes can be an attractive alternative. These include educational programs, marketing and promotion, regulatory measures, and financial incentives. There are various policy measures used to encourage alternative modes of transportation and not only one correct solution. The most effective strategies tend to be customized to the specific target population, work at both the individual and community levels, and incorporate a combination of intervention methods.

A comprehensive analysis of the planned multimodal public transportation HUB

Abstract The research interest in multi-modal passenger transportation planning is growing and dealing with transport infrastructure projects a big number of different challenges have to be considered. Urban transport planning includes scientific and technical knowledge to actions in urban space. In recent years a lot of smart technologies have been promoted for urban problems solving. However, the European Regulation No 1315/2013 takes account of the fact that infrastructure projects need a certain involvement of public and private stakeholders to ensure the promotion of sustainable transport solutions, such as enhanced accessibility by public transport, telematics applications, intermodal terminals/multimodal transport chains, low-carbon and other innovative transport solutions and environmental improvements and the enhancement of cooperation between the different stakeholders. So, including the transport sustainability issues (such as network efficiency, cohesion and environment) in planning process is the obligate requirement for strategic transport planning. The research presents an overview of the case study: planning decisions for the passenger network in Riga City in the frame of the Rail Baltic project – Riga central multimodal public transportation hub. Multi-modal transportation planning should have integrated institutions, networks, stations, user information, and fare payment systems; it is needed to consider all significant impacts, including long-term, indirect and non-market impacts such as equity and land use changes. One of the key-stone question in this case study - multi-modal transportation planning requires consideration of the factors that affect accessibility and whether they are currently considered in planning? The paper presents an integrated analysis in the area of planned transport node compiled from the individual partial studies and based on the desk research. Traffic on macro-level and transit mobility issues are the central aspect in this analysis.

A bi-objective multimodal transportation planning problem with an application to a petrochemical ethylene manufacturer

The aim of this paper is to analyze and improve the freight transportation plan by considering multimodal transportation with maritime, road, and railway transportation. We develop a bi-objective mathematical model, of which objectives are total cost and number of handling operations, an indicator for operational difficulty. The model decides on the optimal multimodal transportation plan including ship chartering and transportation mode decisions. We generate the Pareto frontier to allow an insight into the trade-offs in changing the transportation mix. We test the mathematical model on real-life instance of a petrochemical ethylene manufacturer located in Turkey, originally used in Ozpeynirci et al (Marit Econ Logist 16:188–206, 2014). In addition to Ozpeynirci et al (Marit Econ Logist 16:188–206, 2014), this paper considers a secondary objective and includes rail transport as a third mode in the multimodal transportation mix. In order to analyze the changes in ship chartering and mode selection decisions, we introduce three different scenarios and show that small changes in the transportation mode without restructuring the distribution network may result in significant savings.

Health Impact Assessment of Minnesota's Statewide Multimodal Transportation Plan

Health Impact Assessment of Minnesota's Statewide Multimodal Transportation Plan

Where does bicycling for health happen? Analysing volunteered geographic information through place and plexus

Research on the role of bicycling for health through physical activity has been limited by the lack of information on where bicyclists ride. New big data sources available through smartphone-based applications provide a rich source to provide bicycle volume data more comparable to the scale of information available for automotive and public transit modes. In the case of smartphone apps for fitness tracking, results of this data can be used similar to the growing application of global positioning systems for automotive travel surveying. The authors evaluate data from Travis County, Texas for the purpose of determining where bicyclists ride, primarily for fitness purposes. Ride trip volumes are evaluated with residential and employment density, land use diversity, bicycle facilities and terrain to characterize places chosen for bicycling for health. Though limited to bicycle rides and routes voluntarily logged using the smartphone app, this method provides promise for applications in multi-modal transportation planning and health impact assessment studies.

Enhancing Capacity for Emergency Evacuation through Resource Matching and Coordinated Volunteerism

Many disaster-related large-scale evacuations occur each year, however, most local governments do not have the necessary resources, training or plans to transport individuals without a personal automobile out of a disaster zone. An analysis of previous research suggests that this risk could be addressed by a volunteer-supported, community-based disaster organization coordinating regional multi-modal transportation resources for disaster response. This study introduces and tests the idea of a Transportation Reserve Corps (TRC) by refining its objectives and functions while assessing its value and feasibility. Due to an assortment of potential challenges, a TRC as proposed may not be suitable or sustainable in all geographic or political contexts. Key challenges and other considerations are detailed and synthesized to yield key action steps for further inquiry. This research asserts that, if properly organized, a TRC could be an effective means of evacuating those who cannot self-evacuate during an emergency. Keywords: Multi-modal transportation planning, coordinated volunteerism, emergency evacuation, disaster response, disaster preparedness, carless households

Where Does Bicycling for Health Happen? Analysing Volunteered Geographic Information Through Place and Plexus

Research on the role of bicycling for health through physical activity has been limited by the lack of information on where bicyclists ride. New big data sources available through smartphone-based applications provide a rich source to provide bicycle volume data more comparable to the scale of information available for automotive and public transit modes. In the case of smartphone apps for fitness tracking, results of this data can be used similar to the growing application of global positioning systems for automotive travel surveying. The authors evaluate data from Travis County, Texas for the purpose of determining where bicyclists ride, primarily for fitness purposes. Ride trip volumes are evaluated with residential and employment density, land use diversity, bicycle facilities and terrain to characterize places chosen for bicycling for health. Though limited to bicycle rides and routes voluntarily logged using the smartphone app, this method provides promise for applications in multi-modal transportation planning and health impact assessment studies.