Passenger Preferences Research Articles

Personalized route recommendation for passengers in urban rail transit based on collaborative filtering algorithm

AbstractThe rapid advancements in information technology and intelligent systems within urban rail transit (URT) systems have highlighted the need for more personalized route recommendations that consider passengers’ travel habits. This study aims to address this issue by investigating passenger travel routes alongside other passengers who share similar travel preferences, utilizing collaborative filtering (CF) techniques. The approach involves analyzing historical card data to assess passenger travel profiles, including actual travel time under crowded conditions. By considering both individual passenger preferences and the preferences of similar passengers, a CF algorithm is employed to offer personalized route recommendations. The Shenzhen metro is used as a case study to illustrate the proposed method. The results demonstrate that the proposed approach surpasses traditional route recommendation methods by providing tailored suggestions that align more closely with passengers’ travel preferences. These findings emphasize the value of incorporating passenger travel preferences into route recommendation models, thereby enhancing the accuracy and effectiveness of personalized route recommendations within URT systems.

USERS’ BEHAVIOURAL INTENTIONS TOWARDS PUBLIC TRANSPORT: CITY OF KUALA LUMPUR AS A CASE

Kuala Lumpur is one of Southeast Asia’s most prominent modern cosmopolitan cities, having evolved from the federal capital. In consequence of this transformation, the population of Kuala Lumpur has expanded outward, leading to its city centre being mostly occupied by businesses and residents relocated to suburbs and more affordable areas. Increased commuting distances also facilitated the use of private automobiles which brings about the environmental issues. This research aims to determine factors influencing the behaviour and preferences of public transport passengers particularly Light Rail Transit (LRT) in Kuala Lumpur. In an effort to verify the theoretical model and the relationship between the variables, a structural equation model (SEM) was developed. Perceived Costs (PC), Attractive Alternatives (AA), Feeling towards Transit (FTT), and Service Quality’ (SQ) were considered as the parameters of the public transport services that could be the drivers for the key variables such as Perceived Benefits (PB), Satisfaction (SA), and Behavioural Intentions (BI). This model incorporates direct and indirect effects with the intention of providing insight into how the behaviour of transit passengers is influenced. The results indicate that Service Quality contributes to Satisfaction and Satisfaction contributes to Behavioural Intentions. According to the results, perceived benefits are positively related to satisfaction. The findings do not support the Service Quality and Attractive Alternatives as the significant indicators for the behavioural intentions. The reason for this outcome may have to do with the adverse impact of the COVID-19 pandemic on public transportation services, which in turn affected riders’ perception of these services.

Social Distance Approximation on Public Transport Using Stereo Depth Camera and Passenger Pose Estimation.

In order to effectively balance enforced guidance/regulation during a pandemic and limit infection transmission, with the necessity for public transportation services to remain safe and operational, it is imperative to understand and monitor environmental conditions and typical behavioural patterns within such spaces. Social distancing ability on public transport as well as the use of advanced computer vision techniques to accurately measure this are explored in this paper. A low-cost depth-sensing system is deployed on a public bus as a means to approximate social distancing measures and study passenger habits in relation to social distancing. The results indicate that social distancing on this form of public transport is unlikely for an individual beyond a 28% occupancy threshold, with an 89% chance of being within 1-2 m from at least one other passenger and a 57% chance of being within less than one metre from another passenger at any one point in time. Passenger preference for seating is also analysed, which clearly demonstrates that for typical passengers, ease of access and comfort, as well as seats having a view, are preferred over maximising social-distancing measures. With a highly detailed and comprehensive set of acquired data and accurate measurement capability, the employed equipment and processing methodology also prove to be a robust approach for the application.

Deep learning and thermographic imaging method for thermal comfort prediction in different genders

With the rising demand for electrification and intelligent functions in automobiles, the interest in passenger-centric air conditioning thermal management systems has swiftly increased. However, predicting personalized passenger comfort and applicability continues to pose challenges. The main challenge is how to use valid data to accurately predict the individual needs and preferences of different passengers. In this paper, we developed a deep learning model for a passenger-centric air conditioning system using ResNet50 (a deep residual network architecture consisting of 50 neural network layers). A spatial attention model was also incorporated (Attention-ResNet50) to enhance thermal comfort prediction accuracy. Experiments were conducted to collect basic image data under various thermal comfort and environmental conditions. Infrared images were gathered and classified into cold, comfortable, and warm categories for training, validation, and testing. The accuracy of thermal comfort prediction on testing sets is approximately 5 % higher when incorporating the spatial attention mechanism module compared to the standard ResNet50 model. Thermal comfort tests and predictions were also conducted for different genders. The female thermal comfort prediction model demonstrated significantly higher accuracy levels, nearing 100 % in situations characterized by cold or warm scenarios. The higher accuracy of female comfort prediction results, verifying that female facial features are more sensitive to temperature variation in their surroundings.

Sustainable development of passenger transport in Poland in the context of young passengers’ preference survey

The issue of reducing the adverse impact of transport on the environment has been reflected in key European transport documents. Their content clearly indicates that rail transport is more “environmentally friendly” than road transport. An increase in the number of rail passengers and, at the same time, a drop in the number of road transport passengers is consistent with the concept of sustainable development. The purpose of the article is to analyse the preferences of rail and bus passengers and use research results to enhance sustainable development. In order to achieve this aim, the method of critical analysis of available secondary research results and the survey method were adopted, with the use of a questionnaire in primary research. The author’s own research was conducted on a representative sample of 331 young passengers. On the basis of available secondary research results, the price, travel time and modern technology categories as well as elements thereof were selected for analysis. The study results may be used to formulate a strategy for sustainable development of transport, contributing to the reduction of road passenger transport in favour of rail transport.

Efficient and privacy-preserving multi-agent systems for smart city carpooling with k-regret queries and differential privacy

Multi-Agent Systems are characterized by the presence of multiple independent agents and find diverse applications. In the context of smart cities, MAS is employed in traffic management to enhance operational efficiency, optimize resource utilization, and improve the quality of life for residents. This research paper focuses on the design of a multi-agent intelligent scheduling system, where passengers, vehicles, and carpooling platforms serve as intelligent agents. The primary objective of passengers is to identify suitable shared vehicles based on criteria such as waiting time, budget constraints, and willingness to carpool. Vehicles, on the other hand, organize their schedules based on passenger demands and designated routes. The carpooling platform takes into account resource allocation priority and optimization problems to ensure the efficient operation of the system. To address the issue of vehicle ordering, k-regret queries are utilized, while passenger preferences provide insight into determining loss factors. To safeguard privacy, differential privacy techniques and a random response mechanism are employed when dealing with multiple passenger queries. Furthermore, a direction-preserving insertion verification method is implemented to mitigate computational complexity. The effectiveness and efficiency of the proposed approach are validated through experimentation.

Effects of proactive and reactive health control measures on public transport preferences of passengers – A stated preference study during the COVID-19 pandemic

The outbreak of COVID-19 has caused unprecedented disruptions to public transportation services, passengers' mode choice preferences, and their behavioral pattern. Recent studies have updated the systemic shift from shared travel to private mode due to the perceived higher health risk of shared mobility. However, findings from these studies may not be applicable to public transport-oriented cities with very low rate of private car ownership. In the long term when we arrive at a stabilized ‘New Normal’, the need to promote public transport is likely to be re-emphasized with high priority. This study presents insights into the factors affecting public transport (PT) preferences and potential modal shifts within various modes of PT when proactive and reactive health control measures are implemented. An online stated preference survey was conducted in Hong Kong, where public buses, public light buses, MTR, and taxis are the popular modes of public transport. The regret-based panel mixed multinomial logit model was applied in this study, aiming to capture individual’s regret aversion psychology in the context of the COVID-19 pandemic. The results of our random regret model suggest that travelers generally have lower regrets for the chosen mode when having passenger temperature screening, COVID tracing app, and more frequent disinfection, while seem to be unaware of the in-vehicle air quality. Interaction effects between health control measures and personal characteristics (e.g., age and risk attitudes) were also considered. Trip characteristics, including in-vehicle crowding level, fares, in-vehicle travel time, waiting time at a station, walking time, and trip purpose, were found to influence passengers' choices of travel modes. Policy simulations of possible changes in public transport modal preferences in the presence of various health control measures have also been undertaken. The findings suggest that overall health control measures are appreciated by passengers and do align with promotion of a return to public transport use. However, their effects are quite moderate and should be selectively used for different transport modes. Increasing service frequency can be a promising solution, which reduces crowding and waiting time of passengers using public transport modes, and reduces virus transmission risk. This study contributes to a better understanding of public acceptance and preference toward health control measures in public transport, and call for in-depth cost-benefit analysis in the related fields so that better response can be made in possible future public health crisis.

Aviation Industry and Covid-19: New Normal in the Pandemic Era, the Experience of Passenger, Post-Pandemic Traveling, and Long-term Impact on the Airlines

The Covid-19 pandemic is currently ravaging the entire world and the aviation industry is one of the hardest hit industries. This paper aims to analyze and understand the change in consumers behavior with regards to the aviation industry due to the Covid-19 pandemic. A questionnaire was administered to 100 respondents to glean insights into passenger preferences and travel decisions. The results reveal a strong willingness among respondents to travel when a cure for COVID-19 becomes available, emphasizing the crucial role of medical solutions in boosting passenger confidence. Additionally, the importance of low COVID-19 case counts at destinations and the tension between safety measures and passenger freedom is evident. Moreover, the research highlights the value passengers place on convenience and adaptability, as well as their evolving expectations regarding personal space and hygiene. These findings provide comprehensive insights into the complex dynamics of passenger behavior and satisfaction within the aviation industry during the pandemic, offering valuable reference points for industry stakeholders and policymakers.

Pricing for Refund Service Fee of High-Speed Railway: An Optimization Approach with Uncertain Demand

Compared to single ticket purchase behavior, the impact of ticket cancellation behavior on revenue is full of complexity. Due to the cancelled tickets will be resold with uncertain demand, ticket cancellation behaviors and ticket purchase behaviors are intertwined and influenced, composed of a dynamic and complex system in the presale period. How to charge for ticket cancellation behavior in the name of refund service fee to reduce losses as much as possible is an urgent problem for high-speed railway enterprises. However, there has been little research on this issue. Therefore, a pricing optimization approach for refund service fee based on negative binomial distribution was proposed in this article. Firstly, we proved that the probability of passengers arriving based on the accumulated ticket sales obeyed the negative binomial distribution, which was used to fit the uncertainty demand of passengers. Then, we categorized the passengers to build an optimization model with the objective of maximizing compensation for losses caused by ticket cancellation. A case study was implemented to show that the proportion of refund service fee to ticket price is generally higher than 50%. The refund service fee remains monotonously nondecreasing as the departure date approaches. It also indicated that the current charging standard for refund service fees was too low to offset the losses. In addition, passenger preferences and passenger flow have significant impacts on the dynamic pricing strategy for refund service fees.

Pre-peak fare discount policy for managing morning peak demand of interregional bus travel: a case study in Seoul metropolitan area

ABSTRACT Pre-peak fare discount policies have become increasingly common in urban transit services and show potential to spread morning peak-hour demand. This study analyzes the departure time choice behavior of interregional bus passengers during morning hours. A mixed logit model is applied to estimate the probability of shifting departure time as a function of fare discount rate, in-vehicle congestion, and other influential factors, accommodating heterogeneity of passenger preferences. The analysis data are obtained through a stated preference survey, which is conducted to passengers who regularly use the interregional bus in Seoul metropolitan area. Choice models are segmented by departure time periods (peak, pre-peak, and post-peak) and occupation types (fixed time worker, flexible time worker, and student). Marginal utilities of the unobserved preferences are estimated to suggest policy implications. From the experimental analysis, target passengers for shifting departure time during peak hour are suggested as flexible time workers and students.

Impacts of COVID-19 on Travel Behaviors: The Case of Izmir

With the emergence of the COVID-19 pandemic, passenger preferences for transportation modes and transportation management strategies have rapidly changed in the last few years. In this study, field research was conducted to reveal the behaviors and perceptions of passengers before and during the COVID-19 pandemic in Izmir city of Turkey. A total of 1437 participants were asked to evaluate the pre and during pandemic periods with their frequency of use for different types of transportation modes in Izmir. When the results of the study were evaluated holistically, it was revealed that the participants used environmentally friendly transportation modes such as bicycles and e-bikes more than before the pandemic and that they wanted to use them after the pandemic. Despite this, it is seen that the use of taxis and private vehicles has increased during the pandemic, especially for long-distance trips, and the use of public transport and light rail systems (LRS) in general has decreased. Nearly half of respondents (49.7%) said bus drivers did not use adequate protective equipment during the pandemic. In addition, 41.1% of the participants stated that the HES code application was not applied adequately in public transportation. In the study, it was also revealed that the practice of reducing the number of operational public transport vehicles during the pandemic was found to be negative by the participants.

Research on Choice Preference of Parallel Trains for High-Speed Rail Heterogeneous Passengers

The passenger capacity utilization of parallel trains on China’s high-speed railway (HSR) varies greatly, which seriously affects the revenue and passenger service quality of railroad companies. One of the key factors contributing to this issue is the uniformity of fares. To address this, China’s railway enterprises are implementing differential pricing reforms for parallel trains. This paper presents a two-step method that combines latent class analysis (LCA) and random parameter logit (RPL) models to estimate the preferences of diverse passengers for parallel trains and quantify them using willingness to pay. To illustrate this, we conducted revealed preference and stated preference questionnaire surveys at Nanning East Railway Station, China, focusing on the Nanning-Guangzhou HSR route. These surveys gathered data on passengers’ personal and travel attributes. LCA was carried out according to passenger attributes, and passengers were divided into three categories according to the goodness-of-fit measures. Through the questionnaire data, the travel time and departure time period of each category of the passenger were converted into costs, and the absolute value of each influencing factor of passengers for parallel trains was obtained. Considering the limited rational choice behavior of passengers, the perceived value of each influencing factor of passengers on parallel trains was calculated through prospect theory. Finally, an RPL model for parallel train choice of heterogeneous passengers was constructed, and the parameters of each category of the passenger were estimated based on the absolute value and perceived value of the influencing factors.

How does the state of bus operations influence passengers’ service satisfaction? A method considering the differences in passenger preferences

How does the state of bus operations influence passengers’ service satisfaction? A method considering the differences in passenger preferences

Proposed Business Strategy for Enhancing the Market Competitiveness of Argo Parahyangan Facing the Jakarta – Bandung High-Speed Train

This research analyzes the competition between the Argo Parahyangan train and the Jakarta-Bandung High-Speed Train (HST) on the Jakarta-Bandung route. The objective is to make an informed decision about the continuation of the Argo Parahyangan train, considering passenger preferences. Factors such as accessibility, comfort, travel cost, safety, and travel time influence passengers’ mode of transport choice. The study identifies travel cost and time as primary considerations for passengers. Proposed strategies include optimizing schedules, enhancing customer experience, promoting scenic views, utilizing social media, offering promotions, and forming partnerships. Implementing these strategies would enhance the market competitiveness of the Argo Parahyangan train, operated by the Indonesian Railway Service Company, in response to the Jakarta-Bandung HST operations.

Deep reinforcement learning of passenger behavior in multimodal journey planning with proportional fairness

Multimodal transportation systems require an effective journey planner to allocate multiple passengers to transport operators. One example is mobility-as-a-service, a new mobility service that integrates various transport modes through a single platform. In such a multimodal and diverse journey planning problem, accommodating heterogeneous passengers with different and dynamic preferences can be challenging. Furthermore, passengers may behave based on experiences and expectations, in the sense that the transport experience affects their state and decision of the next transport service. Current methods of treating each journey planning optimization as a non-time varying single experience problem cannot adequately model passenger experience and memories over many journeys over time. In this paper, we model passenger experience as a Markov model where prior experiences have a transient effect on future long-term satisfaction and retention rate. As such, we formulate a multi-objective journey planning problem that considers individual passenger preferences, experiences, and memories. The proposed approach dynamically determines utility weights to obtain an optimal journey plan for individual passengers based on their status. To balance the profit received by each transport operator, we present a variant-based proportional fairness. Our experiments using real-world and synthetic datasets show that our approach enhances passenger satisfaction, compared to baseline methods. We demonstrate that the overall profit is increased by 2.3 times, resulting in a higher retention rate caused by higher satisfaction levels. Our proposed approach can facilitate the participation of transport operators and promote passenger acceptance of MaaS.

Reassigning Departure Slots with Preferences of the Airline and Passengers

The recovery of a departure flight is an important part of airline disruption management. The commonly used optimization objectives for assessing airport slot-scheduling efficiency are delay cost and delay time. However, the current literature does not consider the preferences of airlines and passengers in the recovery process simultaneously. The objective of this paper is to focus on the departure slot reassignment problem and develop a bi-objective optimization model. We introduce a metric for the price of fairness and formulate the airport slot scheduling problem as a bi-objective optimization model that considers the trade-off between the total airline delay cost and total passenger delay time. To quantify the trade-off between total airline delay costs and total passenger delay time, we apply the metric of the price of fairness in the bi-objective model, treating the total airline delay cost as an efficiency metric and the total passenger delay time as a fairness metric, calculating the price of fairness for each Pareto solution. An adaptive non-dominated sorting genetic algorithm-II based on dominant strengths (ANSGA2-DS) is developed to solve this problem. More precisely, three improved operations are presented: the improved fast dominant strength sorting method, new crowding distance improvement, and an adaptive elitist retention strategy. Three scenarios derived from a Chinese airline’s operation data are applied to the proposed bi-objective model and algorithm. The experimental findings demonstrate that the proposed model and method can effectively and efficiently address the problem. This may provide a basis for airline operation controllers to achieve a generally acceptable solution.

Passengers’ preferences for architectural design characteristics in the design of airport terminals

ABSTRACT With the need to increase total revenues, airports have begun to use commercial retail. A well-designed airport may provide great service to clients and consumers and increase their satisfaction and in turn their spending behaviours. Since there is suggestive evidence that there might be cultural associations of white, purple or black with different emotional loadings to these colours across cultures, the present study investigated passengers’ preferences for architectural design characteristics of an airport in a new cultural setting. Discrete choice modelling was used to measure the passengers’ preferences for 10 interior design characteristics of the passenger terminal using 3D renderings. Data on 435 passengers were collected at Mehrabad International Airport, which also included passengers’ emotions. Passengers in the terminal hall preferred a curved and transparent ceiling, material in warm colours, cool lighting, with low width and low height, greenery and without any decorations. When compared to other studies, it may well be that passenger preferences for lighting and certain colour schemes reflect geographical or cultural differences. For other interior design characteristics, preferences relating to the shape of the ceiling and the amount of daylight, as well as the presence of greenery, may well be reflections of passengers’ restorative needs.

Assessing passenger preferences for Bus Rapid Transit characteristics: A discrete choice experiment among current and potential Dutch passengers

To gain ridership, bus services need to offer more than just high frequencies. An attractive system design for Bus Rapid Transit (BRT) is a result of various configurational choices concerning infrastructure, rolling stock and operations. To find out which configurations are preferred by potential and current passengers, a Discrete Choice Experiment was carried out in The Netherlands. For this study, eight BRT characteristics were included. Results (n = 1019) show that four characteristics are valued the most: frequency, service hours, reliability and stop spacing. The attractiveness of three different service formulas or configurations is evaluated. The more conventional bus configuration is preferred by the majority of the respondents. However, a considerable amount (25%) of respondents that prefer this configuration does not consider using this service formula. Configurations that either address offering more passenger comfort or higher capacity, do seem to be attractive to distinct passenger segments who are more likely to actually use the service. These appealing BRT configurations address different types of passenger segments and therefore could coexist on certain routes.

EVALUATING THE INFLUENCE OF GPS-BASED CAB SERVICE ON TRADITIONAL PUBLIC TRANSPORTATION IN GUWAHATI: A CASE STUDY

The emergence of GPS-based cab services has revolutionized the transportation landscape, providing convenient and on-demand mobility options. This study aims to evaluate the influence of GPS-based cab services on traditional means of public transportation in Guwahati, India. Through a comprehensive case study, we analyze the impact of these services on passenger preferences, ridership patterns, and the overall public transport ecosystem. The findings shed light on the transformative effects of GPS-based cab services and their implications for the future of urban transportation. This study aims to evaluate the influence of GPS-based cab services on traditional public transportation in Guwahati, India. Through a comprehensive case study, we analyze the impact of these services on passenger preferences, ridership patterns, and the overall public transport ecosystem. The findings shed light on the transformative effects of GPS-based cab services and their implications for the future of urban transportation. The results reveal a significant influence of GPS-based cab services on traditional public transportation, with passengers exhibiting a growing preference for these services due to convenience, ease of booking, and perceived reliability. The study highlights the need for adaptive strategies to ensure the coexistence and integration of both systems, leading to enhanced urban transportation experiences.

Designing Airline Hub-and-Spoke Network and Fleet Size by a Biobjective Model Based on Passenger Preferences and Value of Time

This study presents a biobjective hub-and-spoke (HS) network design model for the global air passenger networks. The model explores the tradeoff between the total airline cost (airline preference) and lost time cost for passengers (user preference) as the model’s objectives. Most previous studies have focused on airline objectives and established HS networks based on the viewpoint of airlines, despite the importance of passenger objectives. Poor passenger service and inconvenience and dissatisfaction may lead to network breakdown. The major criteria for passenger dissatisfaction in HS networks are schedule and trip delays caused by nondirect flights. These delays (the lost time cost for passengers) are multiplied by the passengers’ value of time (VOT) and minimized as one of the model’s objectives. Another objective that is minimized is the transportation costs of the airline depending on the services provided (short-, medium-, and large-haul flights). The model is solved in a case study (Iranian Aeronautics Network) that is applied to the well-known yearbook of tourism statistics data. Pareto frontier was found for all candidate airports. Also, the number of aircraft required (short-, medium-, and large-haul), as well as the average load factor for different types of aircraft in various weights of the first objective (airline costs), was presented. The results of Pareto frontier indicated that Imam Khomeini International Airport should be selected as the global hub airport for Iran international flight network. Otherwise, Shiraz International Airport and Tabriz International Airport (as the first alternative), as well as Isfahan International Airport and Mashhad International Airport (as the second alternative), would be the best choices. The weight of the first objective (airline costs) seems to be between 0.7 to 1, a practical and logical weight that can reduce passenger costs (as the second objective) by 20% on average by adding only 15 long-haul, 40 medium-haul, and 37 short-haul aircraft to the airline’s fleet. Also, in this range, the average load factor for medium- and long-haul aircrafts is greater than 0.9, which seems to be ideal.