Passenger Mobility Research Articles

Efficiency and Sustainability of Regional Aviation on Insular Territories of the European Union: A Case Study of Public Service Obligations on Scheduled Air Routes among the Balearic Islands

Mediterranean islands of the European Union (EU) have traditionally suffered from a lack of regularity in supplying public transportation due to the high seasonality of the demand for scheduled transport services. The insular fact forces people and goods to be carried either by sea or air, and therefore needs to actively stimulate interest in operating specific routes by proper carriers. As regional economies on insular territories also have a tight dependence on tourism, it is vital to achieve an appropriate balance between the need for effective public transportation and sustainable means of transport. This paper aims to provide an approach to the Public Service Obligation (PSO) system imposed on air routes serving the regional transport needs of the Balearic Islands. This study has analyzed data relating to freight and passenger traffic in the period between 2004 and 2019 from scheduled air services linking Palma de Mallorca with Ibiza and Menorca, as well as those between Menorca and Ibiza, and to their respective short-sea links. Results obtained in the research suggest that PSO impositions, together with significant improvement in the resident subsidy schema (from 50% to 75%), have recently led to a sharp increase in the demand for passenger air services on these routes; thus, avoiding the tender for the award of a public contract. However, it has led to a dramatic fall of freight transport on air routes concerned, as such, public intervention on the air market has only sought to ensure the mobility of passengers.

The impact of COVID-19 on the mobility of Transjakarta passengers

Public transportation functions as an urban hub are designed to help the mobility of urban communities. But today, different things happened. When the COVID-19 virus invades the world and been a pandemic ever since March 2020. In DKI Jakarta implementing Large-scale Social Restrictions policy, it directly reduced the people’s mobility, so that the number of Transjakarta users also declined. Transjakarta is a type of Bus Rapid Transit (BRT). This study aims to analyze what supporting facilities are needed to increase Transjakarta users’ confidence in using Transjakarta as a mode of transportation in daily mobility. This study used a quantitative approach. In this study data analysis was performed using the Importance Performance Analysis model. The study results are the COVID-19 pandemic followed by the Large-Scale Social Restriction policy resulted in a significant reduction in the number of Transjakarta passengers, so it is necessary to provision of public supporting facilities needed by Transjakarta passengers when using Transjakarta in daily mobility to stay safe during this pandemic. The level of importance indicates that the facilities related to health protocols are a priority in the Transjakarta service. Especially in keeping physical distancing. In addition, utility facilities like toilets are needed by passenger.

Assessing City Governance for Low-Carbon Mobility in London

City-level decisions are crucial for delivering a low carbon transition, particularly as urban population dynamics and environments change in response to the COVID-19 pandemic. Ensuring appropriate governance structures, mechanisms and resources to facilitate these decisions is therefore essential. Based on a systematic literature review by van der Heijden (2019), this paper develops a simple framework to assess the state of ten enabling factors for effective urban climate governance, and applies it to low-carbon passenger mobility in London. Drawing on documentary evidence and a series of semi-structured expert interviews, it finds that London’s city authorities have a strong capacity for autonomy, stakeholder participation, local leadership and coordination on climate action and mobility, of which they make extensive use. The national legal and political framework remains broadly supportive following the UK’s departure from the EU, but multi-level co-ordination is thin, and funding issues have intensified conflict over political jurisdiction since the pandemic began. Spatial variation in urban form and infrastructure, coupled with dual-layer city administration, complicate the socio-political landscape and drive for climate action in mobility.

Life cycle environmental impact of a high-speed rail system in the Houston-Dallas I-45 corridor

The Houston-Dallas (I-45) corridor is the busiest route among 18 traffic corridors in Texas, USA. The expected population growth and the surge in passenger mobility may result in a significant impact on the regional environment. This study uses a life cycle framework to predict and evaluate the net changes of environmental impact associated with the potential development of a high-speed rail (HSR) System along the I-45 corridor through its life cycle. The environmental impact is estimated in terms of CO2 and greenhouse gas (GHG) emissions per vehicle/passenger-kilometers traveled (V/PKT) using life cycle assessment. The analyses are performed referring to the Ecoinvent 3.4 inventory database through the phases: material extraction and processing, infrastructure construction, vehicle manufacturing, system operation, and end of life. The environmental benefit is evaluated by comparing the potential development of the HSR system with those of the existing transportation systems. The vehicle component, especially operation and maintenance of vehicles, is the primary contributor to the total global warming potential with about 93% of the life cycle GHG emissions. For the infrastructure component, 56.76% of GHG emissions result from the material extraction and processing phase (23.75 kgCO2eq/VKT). Various life cycle emissions of HSR except PM are significantly lower than for passenger cars.

An Initial Assessment of the Potential Weather Barriers of Urban Air Mobility

Urban Air Mobility (UAM), a subset of advanced air mobility, is a concept that envisions safe, sustainable, affordable, and accessible air transportation for passenger mobility, cargo delivery, and emergency management within or traversing a metropolitan area. In recent years, several companies have designed and tested enabling elements of this concept, including; prototypes of vertical take-off and landing (VTOL) aircraft, operational concepts, and market studies to understand potential business models. While UAM may be enabled by the convergence of several factors, a number of barriers such as weather could present challenges to scaling operations. This research discusses the potential weather and public acceptance challenges for operations in adverse conditions. This article presents a comprehensive seasonal and diurnal climatology analysis using historical observations across anticipated operational altitudes (surface– 5000 ft AGL) at ten metropolitan areas across the United States for the NASA Aeronautics Research Mission Directorate (ARMD). Public perceptions of weather-related societal barriers were evaluated through a five-city general population survey (n = 1,702) where respondents were asked about their views regarding flying in a small aircraft in a variety of adverse weather conditions using a six statement 5-point Likert scale. The results of the climatology analysis found weather most favorable in Los Angeles and San Francisco, with much less favorable conditions in Denver, New York City, and Washington D.C. In the future, equipping automated vehicles, unmanned aircraft systems, and VTOLs with meteorological sensors coupled with machine learning and artificial intelligence could enhance predictive capabilities that reduce flight cancellations and delays for travelers.

Modelling COVID-19 growth cases of provinces in java Island by modified spatial weight matrix GSTAR through railroad passenger's mobility

sThe movement of positive people Coronavirus Disease that was discovered in 2019 (Covid-19), written 2019-nCoV, from one location to another has a great opportunity to transmit the virus to more people. High-risk locations for transmission of the virus are public transportations, one of which is the train, because many people take turns in or together inside. One of the policies of the government is physical distancing, then followed by large-scale social restrictions. The keys to the policy are distance and movement. The most famous transportation used for the movement of people among provinces on Java is train. Here a Generalized Space Time Autoregressive (GSTAR) model is applied to forecast infected case of 2019-nCoV for 6 provinces in Java. The specialty of this model is the weight matrix as a tool to see spatial dependence. Here, the modified Inverse Distance Weight matrix is proposed as a combination of the population ratio factor with the average distance of an inter-provincial train on the island of Java. The GSTAR model (1; 1) can capture the pattern of daily cases increase in 2019-nCoV, evidenced by representative results, especially in East Java, where the increase in cases is strongly influenced by other provinces on the island of Java. Based on the Mean Squares of Residuals, it is obtained that the modified matrix gives better result in both estimating (in-sample) and forecasting (out-sample) compare with the ordinary matrix.

Assessment Urban Transport Service and Pythagorean Fuzzy Sets CODAS Method: A Case of Study of Ciudad Juárez

The purpose of this research article is to provide a comprehensive method of evaluation for public transportation. In this sense, this study considers transport lines that offer in Ciudad Juárez, Chihuahua. Hence, this study presents a description of the public transport system as part of the literature review. Likewise, the document describes an appropriate model based on the more outstanding publications about urban mobility and public transportation for passengers. Nevertheless, it is based on the Pythagorean Fuzzy combinative distance-based assessment (CODAS) to analyze and evaluate the alternatives through criteria that define general performance. Thus, the integration of these methods provides an adequate methodology for decision-making concerning urban planning and mobility to detect and improve the performance of criteria not considered within sustainable urban mobility plans. Results show how the applied approach can work as a powerful tool for the appraisal of the transport service. Finally, the results give relevant information to the local authority of the transport management of Ciudad Juarez to do improvements focused on the user.

FlexPool: A Distributed Model-Free Deep Reinforcement Learning Algorithm for Joint Passengers and Goods Transportation

The growth in online goods delivery is causing a dramatic surge in urban vehicle traffic from last-mile deliveries. On the other hand, ride-sharing has been on the rise with the success of ride-sharing platforms and increased research on using autonomous vehicle technologies for routing and matching. The future of urban mobility for passengers and goods relies on leveraging new methods that minimize operational costs and environmental footprints of transportation systems. This article considers combining passenger transportation with goods delivery to improve vehicle-based transportation. We propose FlexPool: a distributed model-free deep reinforcement learning algorithm that jointly serves passengers & goods workloads by learning optimal dispatch policies from its interaction with the environment. The proposed algorithm pools passengers for a ride-sharing service and delivers goods using a multi-hop transit method. These flexibilities decrease the fleet's operational cost and environmental footprint while maintaining service levels for passengers and goods. The dispatching algorithm based on deep reinforcement learning is integrated with an efficient matching algorithm for passengers and goods. Through simulations on a realistic multi-agent urban mobility platform, we demonstrate that FlexPool outperforms other model-free settings in serving the demands from passengers & goods. FlexPool achieves 30% higher fleet utilization and 35% higher fuel efficiency in comparison to (i) model-free approaches where vehicles transport a combination of passengers & goods without the use of multi-hop transit, and (ii) model-free approaches where vehicles exclusively transport either passengers or goods.

Modeling intermodal travel behavior in an agent-based travel demand model

The topic of intermodal passenger mobility has become more important during the last 20 years. As mobility options increase in number and flexibility, it gets more and more attractive to combine multiple modes on single trips. In addition, intermodal travel behavior is expected to contribute to less car dependent mobility and transport sector’s reduction of greenhouse gas emissions. Creating and improving the conditions for such a behavior requires planning with knowledge about influencing factors and highest resistances. Empirical evidence and behavioral models can support decisions on measures improving intermodal travel supply. This work presents an agent-based model approach containing intermodal travel behavior with regard to its most important decisions. It enables the combination of a multitude of modes and can be extended to even more modes. By combining many decisions and influences it is comprehensible and adaptable to different surveys and circumstances. We show that results are realistic and impacts are valid to be able to forecast effects of potential measures.

Ақылды қаланың интеллектуалды көлік жүйесі моделіндегі үлкен деректерді өңдеу процесі

The article discusses the process of applying big data analysis to solve the problems of traffic analysis in the Smart city of Nur-Sultan. There is substantiated relevance of the model of the intellectual system of development of the transport system of the metropolis. There is carried out review of modern approaches in the field of development and intellectualization of transport systems. The formal development of the system is carried out in a logical-algorithmic language. The article describes its external and internal structure. The proposed solution is based on the Flowmap.blue parallel computing model. There are analyzed two experimental cases such as assessment of the quality of public transport based on the analysis of the history of the location of buses, and assessment of passenger mobility by analyzing the history of ticket purchases from transport cards on weekdays and weekends. Both experiments use a real database of the public transport system in the city of Nur-Sultan, Republic of Kazakhstan.

Passenger rail transport and mobility: user's view about service level and safety

Purpose: presents and categorizes the main problems registered by users of urban trains in the state of Rio de Janeiro, and makes an analysis of episodes of violence, crossing this data with the categories of service levels.
 Methodology: a documentary to verify recorded and reported occurrences on the temporary discontinuation of the users' dissatisfaction service shown in the complaints made in the years 2018 and 2019.
 Findings: security news refers to stations that are located in admittedly violent neighborhoods in the northern part of the city of Rio de Janeiro. Shootings paralyze the circulation of trains and affect the level of services offered by the concessionaire, as they cause the non-compliance with the programmed frequency, the increase in travel time, the feeling of insecurity in stations, trains and integrations and, consequently, the loss of service reliability. .
 Research Limitation / implication: The search referred to the period from January 2018 to December 2019, and applying the filters referring to the central theme, 57 valid news items were obtained for categorization and analysis .
 Originality: contributes to highlight criticisms at the service level and proposes an agenda for studies that contribute to offering safe, comfortable trips, and at acceptable times/costs.

A Multitier Approach to Estimating the Energy Efficiency of Urban Passenger Mobility

As society has experiences new modes of mobility in recent years, cities have planned to increase their energy efficiency as a way of reducing environmental impacts and promoting economic development. However, governments face difficulties in establishing mechanisms to determine the best actions in the management of urban mobility regarding energy efficiency and to elaborate a ranking of cities based on energy efficiency in order to better allocate resources. This is due to the complex nature of obtaining a wide range of activity and energy data from a single municipality, especially in data-scarce regions. This paper develops and applies a model for estimating the energy efficiency of urban mobility that is applicable to different contexts and backgrounds. The main contribution of the article is the use of a multitier approach to compare and adjust outputs, considering different transport configurations and data sets. The results indicate that variations in vehicle occupancy and individual motorized transport rates have a significant impact on energy efficiency, which reached 0.70 passenger-kilometers/MJ in Sorocaba, Brazil. However, as the use of electric vehicles increases in this city, this scenario is expected to change. Additionally, the method has been proven to be an important mechanism for benchmarking purposes and for the decision-making process for transport investments.

Mode effect analysis in the case of daily passenger mobility survey

Abstract In the autumn 2017, The Statistical Office of the Republic of Slovenia (SURS) has conducted for the first time a survey on daily passenger mobility of Slovenian residents. The key statistics are on persons’ daily traveling habits, such as number of trips, travelled distance, time spent on traveling, and so on. Two independent samples were selected for the simultaneous collection of data by two modes, face-to-face interview (CAPI) and online questionnaire (WEB). The goal of this study is to identify the possible sources of mode measurement errors, with the objective to better design and thus improve the whole data collection process. The detailed mode effect analysis is performed by the comparison of the key statistic estimates and the use of regression models. Usually the measurement mode effect is an issue in surveys on the more sensitive topics or persons’ opinions. This work points out that, first, the mode measurement effect can be an issue also in a more factual survey content, and second, the corresponding statistical data processes can have an important contribution to minimising measurement errors. The results show that WEB respondents are inclined to join two or more trips into one reported, which gives lower estimate of average number of daily trips. The main reason is the demanding questionnaire content. Additionally, the complex data editing process was still insufficient to correct completely for such measurement error. The possible improvements of the data collection process are also discussed.

A resilience assessment framework for urban transportation systems

Unexpected disruptions in an urban transportation system may severely damage its structure and functionality. The resilience of urban transportation networks, i.e. the ability to recover from destroyed conditions, is thus significant for the mobility of passengers and freight in cities. However, its definition and measurement have been confused with robustness. This research advances a trackable assessment framework for the resilience of two typical urban transportation modes, i.e. road system and metro system, considering the characteristics of their structure and functionality. Individual vertex-based and edge-based failure models are established for the two transportation systems based on their network models. The proposed measurement approach is applied to an inner-city road freight system and the metro system in Hangzhou, China, to analyse their resilience, considering different combinations of vertex-based and edge-based failures. The proposed approach is compared with an efficiency-based resilience measurement method and outperforms in two parts. First, it has a broader application than previous related methods because it can describe disruptions occurring on one directed vertex or edge using a directed network model. Second, the new method outputs more reliable results due to considering more structural and functional characteristics of urban transportation systems than previous methods.

Toward Sustainable Ferry Routes in Korea: Analysis of Operational Efficiency Considering Passenger Mobility Burdens

With its long coastline, and numerous inlets and offshore islands, coastal ferry industries play a vital role in Korean maritime transportation. This study focuses on the southwestern part of Korea, Mokpo (which has the most inhabited islands and the highest proportion of elderly island residents), and aims to evaluate the impact of passengers’ mobility burdens on the efficiency of ferry routes to achieve a better service for passengers. Integrated principal component analysis–data envelopment analysis and a fuzzy C-means clustering method were applied to analyze the efficiency of ferry routes in the Mokpo area. The efficiency results indicate that longer routes do not always achieve high-efficiency scores. The proportion of general passengers appears to influence the efficiency improvements of both general and subsidiary ferry routes. These findings can assist in better comprehending the relationship between passengers’ mobility burdens and ferry route efficiencies; this will enable the authorities and ferry management departments to develop appropriate policies and strategies and to reconstruct certain features of the inefficient routes, thereby increasing operational efficiency, reducing mobility burdens, and improving the convenience of ferry travel and sustainability of Korean passenger routes.

Analysis of the European international railway network and passenger transfers

• Complex networks analysis is an appropriate tool for studying transport networks. • Railway in europe is strategic for passenger mobility and has not been analysed so far. • A description of the european network is performed and compared with the chinese. • Passenger transfers allows connecting more stations when timetables are synchronised. • Analysis of the connectivity of european cities is carried out considering transfers. Railway transportation is one of the most popular and greenest transportation modes for passengers. Its importance has increased in some regions owing to the deployment of high-speed infrastructures. In Europe, it is a competitive transportation mode for short and medium distance journeys, rivalling airway mode. For topological analysis of transportation networks, Complex Networks Analysis (CNA) appears as a powerful methodology that although used in various circumstances to describe national railway networks, it has not been used thus far at a continental level. In this paper, two topological characterisations of the European International Railway Network are performed using CNA. The first analyses the direct connection among cities in the international railway service using the most commonly used metrics. The results are compared with those of the Chinese Railway Network, of similar size, observing their differences regarding assortativity. The second analysis incorporates passenger transfers between services, discussing how connectivity is improved when timetables are synchronised, remarking the importance of such coordination. Centrality metrics are defined for the assessment of the connectivity of the network. For the sake of realism, thresholds for the maximum reasonable distance to be travelled by train are introduced in the definition of the centrality metrics.

Reinforcement Learning Based Passengers Assistance System for Crowded Public Transportation in Fog Enabled Smart City

Crowding in city public transportation systems is a primary issue that causes delay in the mobility of passengers. Moreover, scheduled and unscheduled events in a city lead to excess crowding situations at the metro or bus stations. The Internet of Things (IoT) devices could be used for data collection, which are related to crowding situations in a smart city. The fog computing data centers located in different zones of a smart city can process and analyze the collected data to assist the passengers how to commute smoothly with minimum waiting time in the crowded situation. In this paper, Q-learning based passengers assistance system is designed to assist the commuters in finding less crowded bus and metro stations to avoid long queues of waiting. The traffic congestion and crowded situation data are processed in the fog computing data centers. From our experimental results, it is found that our proposed method can achieve higher reward values, which can be used to minimize the passengers’ waiting time with minimum computational delay as compared to the cloud computing platform.

The Use of Geolocation to Manage Passenger Mobility between Airports and Cities

A general problem in large cities is mobility. Every day, there are incidents (accidents, construction, or meteorological events) that increase the duration of the journeys in a city and exert negative effects on the lives of citizens. A particular case of this situation is communications with airports. Shuttles are a type of private transport service that operates in airports. The number of passengers carried by shuttles is small. Moreover, shuttles transport passengers to their final destinations and their prices are more competitive than those other private services. However, shuttle services have a limitation with respect to their routes due to the many different destinations of passengers. For this reason, it is important to be able to calculate the best route to optimize the journey. This work presents an Android application that implements a value-added service to plan the route of a shuttle and manage its service (passengers, journeys, routes, etc.). This application combines information from different sources, such as geolocation information from the shuttle driver’s mobile phone, information on the passengers, and results obtained from the service offered by MapBox to calculate the optimized route between several destinations.

The use of steel in the United Kingdom's transport sector: A stock–flow–service nexus case study

AbstractEnergy and material flows and material stocks are key requirements for the supply of goods and services, which in turn support societal development. However, most resource accounting methods restrict the analysis to resource flows, which fails to acknowledge the increasing role of in‐use stocks in service provision. Using the UK transport sector as a case study, we undertook a material flow analysis through the lens of the stock–flow–service (SFS) nexus. We used the latter to identify how steel consumption and accumulation in vehicles contributed to passenger mobility between 1960 and 2015. Our results show that the efficiency of the steel stock contained in cars and motorcycles decreased from 37.5 to 28.0 passenger‐km (pkm)/kg‐year. The steel service for buses decreased from 63.6 to 32.1 pkm/kg‐year, while that of the national railway increased from 23.8 to 70.3 pkm/kg‐year steel. London Underground steel stock–service efficiency improved from 31.5 to 57.0 pkm/kg‐year steel. The annual fraction of flows that maintained the steel stock varied according to vehicle category and was between 3.4% and 8.2%. In terms of the stock expansion rate, the greatest change (on average, an annual increase of 3%) was that of “cars and motorcycles.” This reflects the demographic transitions and the growing consumer demand for car‐based mobility. We discussed how the SFS nexus contributes to a more comprehensive form of resource accounting and reflect upon some of its limitations and how they might be addressed.

A pre-processing and network analysis of GPS tracking data

ABSTRACT Global Positioning System (GPS) devices afford the opportunity to collect accurate data on unit movements from temporal and spatial perspectives. With a special focus on GPS technology in travel surveys, this paper proposes: (1) two algorithms for the pre-processing of GPS data in order to deal with outlier identification and missing data imputation; (2) a clustering approach to recover the main points of interest from GPS trajectories; and (3) a weighted-directed network, which incorporates the most relevant characteristics of the GPS trajectories at an aggregate level. A simulation study shows the goodness-of-fit of the imputation data algorithm and the robustness of the clustering algorithm. The proposed algorithms are then applied to three cases studies relating to the mobility of cruise passengers in urban contexts.