Transport Demand Research Articles

Studying Spatial Unevenness of Transport Demand in Cities Using Machine Learning Methods

The article discusses the issues of spatial unevenness of transport demand in the city by various transport modes. It describes the creation of models using an artificial neural network to estimate the travel time and share by private and public transport in a large city that does not have off-street transport. The city transport macromodel in PTV Visum (V.18) was used as a data source, from which data were obtained on 50 basic parameters taken into account in the specialized software during the development of the transport model. In total, 50 factors act as independent variables that do not have linear relationships with each other and with the dependent variable, which significantly complicates the use of other models. These models allow assessing the influence degree of the most important factors. Further, the article shows the uneven spatial distribution of the shares of trips by private and public transport across the areas of a city. Using machine learning methods, the transport areas of Tyumen were clustered into nine classes belonging to the central sector, where the share of public transport is significantly higher than at the city border. The dependence of the trip share by cars and shuttle buses on the average travel time and distance by private and public transport for each class of transport areas has been established. The research results can be used when creating new transport areas in the city macromodel and when adjusting transport planning documents. The methods used for analyzing big data on the operation of the transport complex can be implemented in the digital twin of the city and the Intelligent Transport System.

Estimating multinomial logit models with endogenous variables: Control function versus two adapted approaches

It is shown that the control function (CF) method’s estimates of the modal constants in a multinomial logit model (MNL) with endogenous explanatory variables are biased. This has not previously been reported in the literature, and has consequences in demand analysis, transportation policy design and project evaluation. Two adaptations of existing approaches are proposed as alternatives to CF for deriving estimators of parameters in MNL models with endogenous explanatory variables that evidence good consistency properties. The first approach is based on moment conditions while the second one combines parameters obtained in two consecutive estimation stages. Both approaches employ instrumental variables. These two adapted approaches are implemented using simulated data from a transport mode choice problem. The results are compared with those obtained using the classic control-function method, typically used by practitioners for estimating transport demand models with endogenous variables and making quantitative evaluations of transport policies and projects. All three approaches generate similar estimates for the parameters of the explanatory variables, but the two proposed adaptations produce considerably more accurate estimates of the modal constants. This greater accuracy has potentially significant consequences for multinomial logit models’ predictive ability and estimates of marginal effects, elasticities and the social benefits of projects based on consumer surplus calculations.

Road Condition Prediction Based on ARIMA Algorithm

This study investigates a road condition prediction method based on autoregressive integral moving average model (ARIMA) and its application in road traffic management and planning. With the acceleration of urbanization and the increasing demand for transportation, effective road condition prediction is crucial for traffic management and planning. This paper first introduces the challenges faced in the field of road transportation. Then, the principles and applications of the ARIMA algorithm in road condition prediction are elaborated, focusing on its advantages in capturing trends and cyclical changes in road traffic data. Subsequently, this paper verifies the effectiveness and usefulness of the ARIMA algorithm in road condition prediction through empirical analysis and case studies. The results show that the ARIMA algorithm exhibits high accuracy and stability in short- and medium-term road condition prediction, providing a simple and effective prediction tool for traffic management authorities and planners. Finally, this paper provides an outlook on the future research direction, presenting research outlooks on model optimization and improvement, combining other methods, and real-time prediction and application, in order to further improve the accuracy and practicality of road condition prediction.

Assessment of the Impact of Intercity Bus Station Relocation on Passenger Flows in Urban Public Transport

Problem. The analysis of existing studies on transport hub operation in a city as well as on the methods for modelling the transport demand they generate showed that there are no specific recommendations for assessing the impact of the intercity bus station location on the trip patterns in urban transport system. This caused the need to search for a tool suitable to assess the influence of intercity bus station relocation on passenger service and passenger flows in the urban transport system. This paper shows the results of addressing this issue respective to urban public transport operation. Goal. The goal of the paper is to model the passenger flows to and from intercity bus stations considered transport anchors in urban public transport system as well as estimate the impact of station relocation on passenger flows and quality of service. Methodology. To achieve the stated goal, a survey of intercity passengers at the bus stations in the city of Rivne was made. This allowed for collecting the distances of the trips to and from existing bus stations and fitting the theoretical distribution to describe them. This distribution was used as the deterrence function during trip matrix calculation, which was required for the development of the public transport model. The model made it possible to quantify the potential impact of the Bus Station “Rivne” relocation on the urban transport system. Results. The considered bus station relocation can cause a decrease in the quality of service for passengers travelling to and from the station by public transport due to a probable significant increase in the average travel time and distance, the number of transfers, the passenger hours spent on travelling, and the passenger-kilometres covered to ensure the trips under study. Originality For the first time, the quantitative estimation of the impact of bus station relocation based on the regularities in the lengths of the trips to and from the stations was obtained. Practical value. The obtained trip length distribution can be used to calculate the origin-destination matrix, develop the public transport model, and quantify the probable consequences of the transport hub relocation.

Twitter, Instagram, Youtube Speak: Understanding Sentiments on LRT Jabodebek Services via Inset Lexicon, IndoBERT and BERTopic Approaches

Rapid urbanization in the Jabodetabek region has led to an increased demand for public transportation. Responding to this need, the government has initiated the development of a new public transportation mode, namely the LRT Jabodebek. However, as a new public transportation mode, the LRT Jabodebek has both strengths and weaknesses in serving the community. Various public comments are expressed through social media platforms. To enhance service quality, it is crucial to pay attention to public comments. Therefore, a sentiment analysis is required to identify and delve into both positive and negative sentiments regarding the LRT Jabodebek service through comments on Twitter, Instagram, and Youtube. The methodology involves a combination of Lexicon-based, IndoBERT model, and BERTopic approaches to gain a deeper understanding of the Jabodebek LRT service trends. The study reveals that 55.9% of the 8,523 comments carry a negative sentiment, and the IndoBERT model achieves an accuracy of 85.97% in sentiment classification.

Compensating energy demand of public transport and yielding green hydrogen with floating photovoltaic power plant

The last three decades have seen a dramatic increase in the renewable energy sector as a result of increased human energy consumption and environmental concerns about fossil fuels. Offshore renewable energy sources are the most alluring and promising technologies because of more energy potential, less space, and visual restrictions than onshore ones. Among those, floating solar photovoltaic (FPV) has a remarkable reputation. The present study focuses on a viable way to replace energy resources derived from fossil fuels with renewable solar energy. In this regard, electrical energy demand is investigated where a floating photovoltaic system and integrated hydrogen production unit are employed on water surface of Yamula Dam. Energy demand of public trams would be compensated with electricity generated by FPV and rest of energy would be utilized for hydrogen production. Key results illustrated that in various scenarios, the energy generation amounts were around 31×106 kW, 32×106 kW, and 39×106 kW, while the energy consumption amounts were approximately 24×106 kW. It was evident that the energy created more than offset the amount consumed. It was also note that the total costs of entire system were $94.1 M, $78.5 M and $71.2 M according to the different cases. It was also observed that in October and November, the remaining energy from the Bozankaya tram produced the most hydrogen with 125 kg, whereas in September and October, the remaining energy from the Sirio tram produced approximately 70 kg.

LNG Logistics Model to Meet Demand for Bunker Fuel

The main objective of this manuscript is to build a model for the distribution of LNG as a marine fuel in the southern Baltic Sea based on a genetic algorithm in terms of cost. In order to achieve this, it was necessary to develop, in detail, research sub-objectives like analysis of the intensity of ship traffic in the indicated area and analysis of LNG demand in maritime transport. In the first part of this study, the authors use data from the IALA IWRAP Mk2 and the Statistical Office in Szczecin to analyse the marine traffic density (by type of vessel) in the southern part of the Baltic Sea. LNG used as marine fuel reduces toxic emissions into the atmosphere. The authors specify the LNG fleet size and locations of LNG storage facilities in a way to ensure that the defined LNG bunker vessels can supply fuel to LNG-powered vessels within the shortest possible time period. The database contains a set of traits necessary to determine the optimal demand for LNG. The traits were developed based on an existing LNG fleet and appropriately selected infrastructure, and they represent existing LNG-powered vessels as well as LNG bunker vessels and their specifications. Based on the created LNG distribution model, were performed in Matlab R2019a software. An LNG distribution model was developed, which uses a genetic algorithm to solve the task. The demand for LNG for the sea area under analysis was determined based on data on the capacity of LNG-powered vessels (by type of vessel) and their distance from the specified port.

ROBUST OPTIMIZATION MODEL OF CONTAINER LINER ROUTES IN FEEDER LINE NETWORK

The universal application of the hub-and-spoke maritime network makes feeder line network key to restricting the quality and efficiency of maritime transportation. However, container liner routes in feeder line network are susceptible to the changes in shipment demand and international fuel prices. Therefore, based on the hub-and-spoke maritime network, this paper constructs a robust optimization model of container liner routes in feeder line network. Under the capacity and time constraints, routes optimization and ship equipment under uncertain environment are analysed. An improved tabu search algorithm was designed based on the characteristics of the model. The example analysis proves that the model can still ensure the robustness of routes under uncertain environment, which is more applicable than the deterministic model.

Optimal PV‐storage capacity planning for rail transit self‐consistent energy systems considering extreme weather conditions

AbstractWith the rapid development of electrified rail transportation, the traction load demand of rail transportation has increased sharply, and its operational security under extreme conditions has been highlighted. Given the above background, this paper proposes a planning method for the optimal photovoltaic (PV)‐storage capacity of rail transit self‐consistent energy systems considering the impact of extreme weather. First, the basic structure of a rail transit self‐consistent energy system is presented. Second, considering a power transmission system with line trip‐off under extreme weather conditions, a traction load reduction model is established to obtain the maximum power exchange capability between the power transmission network and rail substations. Subsequently, an optimal planning model for a hybrid energy storage system (HESS) is proposed to minimize the total HESS investment and rail transit system operation costs. Finally, the model is linearized as mixed‐integer linear programming and solved using Gurobi and the Yalmip toolbox. The simulation results verify the effectiveness of the proposed optimal PV‐storage capacity planning for rail transit self‐consistent energy systems.

NEW URBANISM IN CONTEXT TO MOBILITY

In recent years, the concept of New Urbanism has gained significant attention as a means of addressing the challenges of urban development and mobility. There has been a growing interest in understanding how new urbanism principles impact mobility patterns and behaviours. The New Urbanism, initially conceived as an anti – sprawl reform movement, New Urbanism is an urban planning and design approach that emphasizes walkability, mixed land uses, and connectivity. In new urbanism, there is a strong emphasis on promoting alternative modes of transportation to reduce car dependency and create more sustainable and liveable communities. These abstract aims to explore the relationship between new urbanism and mobility, focusing on the various factors that influence transportation. This paper aims to explore the key principles of New Urbanism that enhance mobility and examine the ways in which it promotes the use of alternative modes of transportation. Through this research, we hope to provide insights into the potential benefits and challenges of New Urbanism as a means of improving mobility in urban areas. Some common modes of transportation in new urbanist developments include walking, cycling, and public transit by examining the role of street design, infrastructure, and transportation demand management strategies. This not only helps to reduce traffic congestion and air pollution but also promotes a healthier and more active lifestyle. The goal is to encourage alternative transportation options. This research seeks to provide insights into creating more sustainable and accessible urban environments. This research will contribute to the ongoing dialogue on how to design cities that prioritize mobility, promote alternative transportation options, and reduce car dependency. Key Words: New urbanism, Sustainable mobility, Transit-oriented development (TOD), Walkable communities, Active transportation, public space, public transit.

Optimizing School Bus Routes With Ant Colony Optimization: A Dynamic Approach To Sustainable Transportation Logistics

In response to the escalating demands of modern transportation and logistics within the educational sector, this paper presents a novel exploration of Ant Colony Optimization (ACO) applied to school bus routing, a complex subset of the Vehicle Routing Problem (VRP). Traditional methods of addressing VRPs have proven insufficient in navigating the intricate requirements and dynamic nature of routing challenges, necessitating the adoption of more sophisticated heuristic and metaheuristic strategies. By harnessing ACO, inspired by the path-finding capabilities of ants via pheromone trails, this study introduces an innovative approach to dynamically optimize school bus routes. This optimization not only aims for efficiency in routing but also emphasizes environmental considerations and cost reduction. This research extends beyond the theoretical framework of ACO, incorporating practical applications and simulations that reflect real-world conditions such as fluctuating student attendance and varying traffic patterns. Through a comprehensive analysis that includes the development of a Python-based ACO model, calculation of transition probabilities, and a simulation of routing strategies, we demonstrate the algorithm's robustness and versatility. Additionally, we address critical factors such as time windows and bus capacity constraints, underscoring the model's adaptability to the multifaceted dimensions of school bus routing. The findings from this study highlight the significant advantages of applying ACO to VRPs, showcasing notable improvements in route efficiency, fuel consumption, and overall logistical execution compared to conventional routing methods. This research not only contributes to the existing body of knowledge on VRPs and ACO but also sets the stage for future explorations into dynamic routing optimization. It opens avenues for integrating advanced predictive models and real-time data analysis, promising further enhancements in transportation logistics and the potential for broad application across various sectors.

Public Transport Accessibility Policy for Disabled Batam City in the Concept of Sustainable Transportation

This study evaluates sustainable transportation development in Batam City, focusing on the accessibility of public transportation for persons with disabilities as a sustainability indicator. Utilizing qualitative research methods, primary data were collected through interviews, complemented by secondary data from various sources such as Department of Transportation reports, Batam City Government documents, and online media. Data analysis employed triangulation, supported by Vosviewer Tools for processing secondary data. Findings reveal inadequate transportation accessibility services for people with disabilities in Batam City. Effectiveness, efficiency, adequacy, and equality for this demographic remain suboptimal, indicating a lack of sustainable and inclusive approaches. Regional regulations concerning disabilities are not fully implemented, and there's a lack of management strategies for transportation or Transport Demand Management tailored to assist people with disabilities. The Bus Rapid Transit (BRT) system's ineffectiveness hampers transportation connectivity, exacerbating access challenges. Essential disability facilities like ramps and bell signals are unevenly provided, while the limited number of special seats creates difficulties in prioritizing pregnant women and the elderly. Consequently, the current serviceability access falls short of meeting the standard needs of people with disabilities, as envisioned in Sustainable Transportation frameworks.

ANALISIS KINERJA KEUANGAN MENGGUNAKAN METODE EVA PADA PERUSAHAAN PELAYARAN SEKTOR ENERGI YANG TERDAFTAR DI BEI PERIODE 2019-2022

Abstract, This study aims to determine and analyze financial performance using the Economic Value Added (EVA) method in energy sector shipping companies listed on the Indonesia Stock Exchange (IDX) for the 2019-2022 period. This type of research is qualitative with a descriptive approach. The population in this study were energy sector shipping companies listed on the Indonesia Stock Exchange (IDX) totaling 19 companies. The sample in this study were 13 companies determined by purposive sampling method. The type of data used is secondary data. The results showed that financial performance using the Economic Value Added (EVA) method in energy sector shipping companies listed on the Indonesia Stock Exchange (IDX) for the 2019-2022 period fluctuated. The occurrence of the COVID-19 pandemic and the decline in demand for oil, gas and coal transportation made the company experience a decrease in revenue. Nevertheless, the company continues to strive to improve its performance by implementing and planning strategies and policies to be better in the future.

INTEGRATION OF AN URBAN TRANSPORTATION SYSTEM AS AN ELEMENT OF MOBILITY PLANS

In today’s urban mobility scene, the increasing number of private cars trying to travel through the city centers is one of the most onerous problems facing urban transport. Knowing about the impact that different transport demand management models can have on the efficiency of the urban transport system may be of key importance for developing solutions in this area. This knowledge is the main goal of the considerations undertaken. The observations presented in this article took place during the formation of a Sustainable Urban Mobility Plan (SUMP) for the Bielsko-Biała agglomeration. The author of the article is a member of the working group that prepared the SUMP project for the city. The study examined the effectiveness of mobility planning activities. The scope of these activities included collecting the data necessary to create a mobility plan, analyzing this data, building scenarios in the area of transport demand management, and designing the assessment of the adopted solutions. The article shows the procedure for creating a model of a SUMP for urban areas, which may be a prerequisite for municipal governments for effective applications for funding projects related to transport. The most important part of the analysis is the evaluation using integration indicators of selected key elements of the urban transport system of the Bielsko-Biała agglomeration in the area of transfer accessibility. Such research allows us to answer the question of whether the locations of new railway stops proposed under the agglomeration SUMP contribute to increasing the integration of the urban transport system. The current research implements one of the dimensions of mobility management projects. Related goals are, among other things, to achieve more effective integration of the urban transport system. The analyses were carried out by measuring selected properties of the transport network and a survey questionnaire.

Modeling airport choice for a multi-airport area using a random parameter logit model

Recent years have seen a growing demand for air transport which has led to an increase in the number of airports providing a significant number of flights in Spain and throughout the rest of Europe. Choosing which airport to use depends on the competition between the airlines operating from them and how accessible they are to the residents in the surrounding areas. This research introduces an efficient design based on D-error using a stated preferences survey to study user behaviour when choosing between competing airports. The data is modelled using a Multinomial Logit Model and a Random Parameter Error Component Logit (RPECL), a discrete choice model which allows us to study user preferences taking into account their varying systemic and random tastes, as well as find specific correlations which may exist between similar alternatives. This meant the authors were also able to calculate the direct and cross elasticities to examine the weight of the different variables involved in making the choice of airport. The results showed, for a study case in Spain (Bilbao and Seve Ballesteros - Santander airports), that increased access time to the furthest airport caused an important fall in demand (choice) for that airport. It was also shown that access fees had a lower elasticity than the flight cost and that the fewer the transfers required to reach the final destination increased the probability of choosing an airport. Therefore, in order for smaller airports to compete with larger airports they need to invest more in reducing the cost of flights, attracting more low-cost companies and offering more direct complementary destinations than those provided by larger airports.

Design of mixed fixed-flexible bus public transport networks by tracking the paths of on-demand vehicles

On-demand ridepooling (ODRP) vehicles follow routes that are fully flexible. However, when the system does not provide door-to-door service and users can be asked to walk, their paths tend to concentrate, particularly along main streets that connect highly demanded areas of the city. These frequently travelled segments are hence useful to multiple passengers, which can be used as an indicator that it would be efficient to allocate a fixed public transport line there.In this paper, we formalise this idea and propose a novel method to design a public transport network, where bus fixed lines are combined with ODRP. Given a network and a transport demand, we first simulate how to serve it using only ODRP with walking. For this, we employ a state-of-the-art assignment algorithm, and take as output the resulting users’ paths. These paths are then processed by a tailored algorithm to create fixed lines where the paths accumulate the most. Users who do not have an available fixed line (i.e., those whose paths were barely shared) are served by ODRP in the mixed system. Simulations using real-life data from Utrecht, The Netherlands, and the Sunshine Coast, Australia, reveal the merits of our method compared to several benchmarks. Crucially, our method builds a small number of fixed lines while still serving the majority of the demand through them.This study contributes not only to the design of public transport networks, but also to the understanding of the patterns that naturally appear in intrinsically flexible mobility systems.

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

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

Estimating On-Road Transportation Carbon Emissions from Open Data of Road Network and Origin-Destination Flow Data

Accounting for over 20% of the total carbon emissions, the precise estimation of on-road transportation carbon emissions is crucial for carbon emission monitoring and efficient mitigation policy formulation. However, existing estimation methods typically depend on hard-to-collect individual statistics of vehicle miles traveled to calculate emissions, thereby suffering from high data collection difficulty. To relieve this issue by utilizing the strong pattern recognition of artificial intelligence, we incorporate two sources of open data representative of the transportation demand and capacity factors, the origin-destination (OD) flow data and the road network data, to build a hierarchical heterogeneous graph learning method for on-road carbon emission estimation (HENCE). Specifically, a hierarchical graph consisting of the road network level, community level, and region level is constructed to model the multi-scale road network-based connectivity and travel connection between spatial areas. Heterogeneous graphs consisting of OD links and spatial links are further built at both the community level and region level to capture the intrinsic interactions between travel demand and road network accessibility. Extensive experiments on two large-scale real-world datasets demonstrate HENCE's effectiveness and superiority with R-squared exceeding 0.75 and outperforming baselines by 9.60% on average, validating its success in pioneering the use of artificial intelligence to empower carbon emission management and sustainability development. The implementation codes are available at this link: https://github.com/tsinghua-fib-lab/HENCE.

Investigating effective competition in the Swedish freight market in the presence of network effects, using the case of road transport

The purpose of this paper is to investigate effective competition in the Swedish freight market in the presence of network effects, taking road transport as an example. This study used the extended version of Wandel’s three-layered transport system model as its research framework, on which the construction of a structural equation modeling (SEM) approach was built. Four variables were chosen, with their annual sample data drawn from Swedish official statistics: transport work to represent the integrated transport demand, length of transport infrastructure network to address the effect of infrastructure network on the integrated transport demand, energy consumption of Swedish registered trucks to address the effect of energy distribution network on the integrated transport demand, and public spending on investment and maintenance of infrastructure as a basis for investigating effective competition. We found positive effects of road infrastructure network and road energy distribution network on the integrated transport demand. Our result also shows that the public spending on investment and maintenance of infrastructure has a positive effect on the integrated transport demand through the length of infrastructure network. We argue that the Swedish road transport market is less effective when transport network effects are present, based on the definition of effective competition and our assumption. We discovered a gap in the economic literature between the concepts of effective competition and network effect within the context of freight transport, and we suggested a new category in effective competition to supplement performance, conduct, and structure categories; this new category should take into account the pre-existing conditions of network effect for existing competitors and newcomers, with its various features such as accessibility (i.e., network size), connectivity (i.e., network density), and network design and operation.

A CsPbBr3/CdS-based hybrid bidirectional optoelectronic device with light-emitting, modulation, and detection functions

Optoelectronic integrated circuits, with a broad photonic transportation bandwidth, have emerged as a promising solution to fulfill the escalating demands for high-volume information transportation and processing. However, challenges persist in developing optoelectronic integrated circuits based on low-dimensional nanostructures, including limited integration density and high energy consumption. Here, we demonstrate a bidirectional optoelectronic device by integrating a light-emitting/harvesting CsPbBr3 nanoplate with a waveguiding/modulating/detecting CdS nanobelt. By configuring the CsPbBr3 nanoplate in a Schottky-type device structure with a metal electrode, bright electroluminescence was attained at a bias voltage of 18 V. Thanks to the electric field-tuned phonon-coupling effect, the waveguided light in the CdS nanobelt exhibited a high modulation depth of up to 94%, rendering it an excellent building block as optical modulators and optical switches. Moreover, the integrated nanostructure device showcased functionality in the photodetection mode. The proposed device architecture holds promise for broader applications, potentially extending to other perovskite-coupled II–VI semiconductor optoelectronic integrated circuits for expanding integration capacity and enhancing optoelectronic performance.