Passenger Destination Research Articles

Fuzzy-theory-based pedestrian dynamics models for studying the waiting passenger distribution at the subway platform

The behavior of waiting area choice of passengers at the subway platform can directly affect the travel efficiency, thereby affecting the service level of the station. Combining fuzzy logic theory and cellular automata model/social force model, a waiting area choice model for passengers at the subway platform is proposed, which can be used to predict the distribution of passengers. The distance from passengers to waiting areas, the number of passengers in the waiting area, and the length of the remaining waiting area are selected as the main influence factors after the analysis of field investigation, which are defined as the input variables of the fuzzy logic system. The choice probability of passengers to each waiting area is defined as the output variable. The waiting area with the highest probability is taken as the destination of passengers, thereby the static field value in the cellular automata model and the desired direction in the social force model can be determined. The total error of the established method can be as low as 10.13%, which shows a relatively good prediction effect. The effects of passenger attributes, stair attributes, and passable area size on the waiting area choice behavior are simulated. The results show that the influence degree of passenger companion behavior on passenger distribution depends much on the proportion of passengers in the company. The body radius and the desired speed of passengers have a great impact on the distribution balance of waiting passengers based on the analysis of the Gini coefficient and random forest method. The interval of multiple stairs should be the same as far as possible. The setting of a passable area is conducive to the flow of passengers during peak hours. The number of passengers is most pronounced in the distribution balance of passengers by comparing five different influence factors. Guidance strategies can be released through the LED display screen on the basis of the predicted distribution information. This paper provides a new idea for modeling the waiting area choice behavior of passengers, which lays a theoretical basis for the design and transformation of platform facilities.

Utilizing Wi-Fi Sensing and an Optimized Radius Algorithm to Count Passengers with Transfers to Enhance Bus Transit O-D Matrix

The origin and destination (O-D) of public transit passengers are important for the planning and operation of the transit system. However, only 46% of public transit agencies have a smart card system in the US, and most of them require an entry-only tap, which prohibits identifying passenger destinations unless utilizing an estimation model. Therefore, there is a need for a cost-effective and automated solution to facilitate the majority of the US transit agencies in recognizing the origin and destination of passengers as well as capturing passenger transfers. This paper created a novel algorithm for transit agencies to count passengers with the consideration of transfers using a cost-effective Wi-Fi sensing–based approach. Two pilot studies were conducted in the city of Louisville, Kentucky on three different bus routes to explore the feasibility of the method. A Wi-Fi detector was installed in the bus to detect passengers, and a manual counting was performed to be used as ground truth data. After data collection, the proposed algorithms were applied to optimize the detection radius and to eventually find origins, destinations, and transfers. Analysis revealed that the proposed Wi-Fi–based approach is capable of recognizing 78.7% of the total passengers as well as detecting their boarding and alighting activities. The paper demonstrates the ability of the proposed method to detect passengers with a reasonable detection rate by using Wi-Fi technology on bus routes, which makes it feasible for transit agencies to conduct frequent and low-cost network-level passenger O-D studies.

Pengaruh Kinerja Petugas dan Fasilitas Pelayanan Ruang Tunggu terhadap Kepuasan Penumpang di Bandar Udara Sultan Muhammad Salahuddin Bima

The Effect of Officer Performance and Waiting Room Service Facilities on Passenger Satisfaction at Sultan Muhammad Salahuddin Airport, Bima. Author : Abdurrahman. The progress of science and innovation is currently getting faster and changing. This is very much felt in the field of aviation, both local airlines and logging around the world. The need for a destination is also felt by passengers to choose transportation services including air transportation that is able to carry their journey to reach the passenger's destination quickly, safely and fairly according to their financial needs. To see if the performance of officers and service facilities has a synchronous impact on passenger satisfaction at Sultan Muhammad Salahuddin airport, Bima. This study uses quantitative testing with an overview of the selected strategy as important information using a questionnaire. The population that will be used in this study are passengers who use waiting room facilities at Sultan Muhammad Salahuddin airport, Bima. on a sample of 60 respondents. The data analysis technique used the help of IMB SPSS statistics21. The results showed a significance value of 0.000 <0.05. This shows that there is a positive influence on the performance of officers and waiting room service facilities on customer satisfaction at Sultan Muhammad Salahuddin airport, Bima. Based on the Officer Performance variable (X1), the t-count is 4.817, the service facility variable (X2) is 5.311 t-count is obtained which is considered significant 0.000 <0.05 so H1 or H2 is accepted, meaning that the Officer Performance variable (X1) and service facilities ( X2) waiting room has an effect on passenger satisfaction (Y). From the results of this review, it is known that the calculated F value is 54,025 with a total significance of 0.000 <0.05. So it can be concluded that the variables of Officer Performance (X1) and Service Facilities (X2) waiting room significantly have a positive and significant influence on Passenger Satisfaction (Y) at Sultan Muhammad Salahuddin Airport Bima.
 Keyword: Kinerja, Fasilitas Pelayanan, Kepuasan penumpang

Passenger Destination System for Commercial Vehicles

Public vehicles are predominantly the commonest and most widely used means of transport for humans in this current time. However, passengers who often commute with public vehicles mostly encounter the challenge of missing their destination primarily due to difficulties in communicating their destination to the driver, sleeping in the case of long-distance travel or the inability to recognize their destinations, and many more. In this paper, a fully functional and cost-effective passenger destination alert system for commercial vehicles (PDSCV) was developed. This designed passenger destination alert system provides solutions to the problems and as well meets the needs of some target groups such as the physically challenged. To solve these challenges, a GPS-based location module interfaced with a microcontroller unit was employed in the hardware prototype design. Destination stations were displayed and announced via an LCD and audio system respectively to generally alert all passengers on board the vehicle. The prototype also includes alert buttons beside every seat which were to be used by passengers in emergencies and to alert the driver to skip a particular terminal provided the bus is fully occupied and no passenger intends to alight. In addition to the hardware prototype, a software application was developed to provide further information to first-time travellers. The test and results from the entire system indicate a bridge in the communication gap between the driver and the passengers of commercial vehicles.

A Regional View of Passenger Air Link Evolution in Brazil

Passenger flows among cities, at both the domestic and international levels and in different countries and regions, have been studied for forecasting purposes. This paper seeks not a forecasting mechanism, but to understand, by analyzing passenger origins and destinations by Brazilian sub-region, how Brazil’s domestic air passenger network links have evolved. Using income, population, and fare price as inputs, and seats sold as output, air link performance is examined by data envelopment analysis to discuss the regional link of domestic passenger traffic in Brazil and its dynamics, considering two specific years. The findings indicate that, although the highest passenger flow density is concentrated in Brazil’s Southeast region, performance by emerging origins and destinations (O-Ds), such as those connecting the Northeast, display more substantial strength indices and advances (Malmquist analysis). The analysis of specific links was also important, which showed that important Brazilian airports are not necessarily more competent in generating trips. The Catch-Up indicator for innovation reveals the weak point in Brazil’s air transport network. Although some airports enjoy strong networkability, they do not correspond in passenger origin or destination strength.

Optimal assignment of buses to bus stops in a loop by reinforcement learning

Bus systems involve complex bus–bus and bus–passengers interactions. In this paper, we study the problem of assigning buses to bus stops to minimise the average waiting time of passengers. We formulate an analytical theory for two specific cases of interactions: the normal situation, where all buses board passengers from every bus stop, versus the novel “express buses” where disjoint subsets of non-interacting buses serve disjoint subsets of bus stops. Our formulation allows for the exact calculation of the average waiting time for general bus loops in the two cases examined. Compared with regular buses, we present scenarios where “express buses” show an improvement in terms of average waiting time. From the theory we can obtain useful insights: (1) there is a minimum number of buses needed to serve a bus loop, (2) splitting a crowded bus stop into two less crowded ones always increases the average waiting time for regular buses, (3) changing the destination of passengers and location of bus stops do not influence the average waiting time. Subsequently, we introduce a reinforcement-learning platform that can overcome the limitations of our analytical method to search for better allocations of buses to bus stops that minimise the average waiting time. Compared with the previous cases, any possible interaction between buses is allowed, unlocking novel emergent strategies. We apply this tool to a simple toy model and three empirically-motivated bus loops, based on data collected from the Nanyang Technological University shuttle bus system. In the simplified model, we observe an unexpected strategy emerging that could not be analysed with our mathematical formulation and displays chaotic behaviour. The possible configurations in the three empirically-motivated scenarios are approximately 1011, 1011 and 1020, so a brute-force approach is impossible. Our algorithm can reduce the average waiting time by 12% to 32% compared with regular buses and 12% to 29% compared with express buses. This tool can have practical applications because it works independently of the specific characteristics of a bus loop.

Revisiting the Effects of High-Speed Railway Transfers in the Early COVID-19 Cross-Province Transmission in Mainland China.

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic that was reported at the end of 2019 in Wuhan, China, and was rapidly disseminated to all provinces in around one month. The study aims to assess the changes in intercity railway passenger transport on the early spatial transmission of COVID-19 in mainland China. Examining the role of railway transport properties in disease transmission could help quantify the spatial spillover effects of large-scale travel restriction interventions. This study used daily high-speed railway schedule data to compare the differences in city-level network properties (destination arrival and transfer service) before and after the Wuhan city lockdown in the early stages of the spatial transmission of COVID-19 in mainland China. Bayesian multivariate regression was used to examine the association between structural changes in the railway origin-destination network and the incidence of COVID-19 cases. Our results show that the provinces with rising transfer activities after the Wuhan city lockdown had more confirmed COVID-19 cases, but changes in destination arrival did not have significant effects. The regions with increasing transfer activities were located in provinces neighboring Hubei in the widthwise and longitudinal directions. These results indicate that transfer activities enhance interpersonal transmission probability and could be a crucial risk factor for increasing epidemic severity after the Wuhan city lockdown. The destinations of railway passengers might not be affected by the Wuhan city lockdown, but their itinerary routes could be changed due to the replacement of an important transfer hub (Wuhan city) in the Chinese railway transportation network. As a result, transfer services in the high-speed rail network could explain why the provinces surrounded by Hubei had a higher number of confirmed COVID-19 cases than other provinces.

Alighting Stop Determination of Unlinked Trips Based on a Two-Layer Stacking Framework

Smart card data of conventional bus passengers are important basic data for many studies such as bus network optimization. As only boarding information is recorded in most cities, alighting stops need to be identified. The classical trip chain method can only detect destinations of passengers who have trip cycles. However, the rest of unlinked trips without destinations are hard to analyze. To improve the accuracy of existing methods for determining alighting stops of unlinked trips, a two-layer stacking-framework-based method is proposed in this work. In the first layer, five methods are used, i.e., high-frequency stop method, stop attraction method, transfer convenience method, land-use type attraction method, and improved group historical set method (I-GHSM). Among them, the last one is presented here to cluster records with similar behavior patterns into a group more accurately. In the second layer, the logistic regression model is selected to get the appropriate weight of each method in the former layer for different datasets, which brings the generalization ability. Taking data from Xiamen BRT Line Kuai 1 as an example, I-GHSM given in the first layer has proved to be necessary and effective. Besides, the two-layer stacking-framework-based method can detect all destinations of unlinked trips with an accuracy of 51.88%, and this accuracy is higher than that of comparison methods, i.e., the two-step algorithms with KNN (k-nearest neighbor), Decision Tree or Random Forest, and a step-by-step method. Results indicate that the framework-based method presented has high accuracy in identifying all alighting stops of unlinked trips.

Taxi-Passenger’s Destination Prediction via GPS Embedding and Attention-Based BiLSTM Model

The prediction of taxi-passenger's destination with the partial GPS trajectory left by moving taxis is an important yet challenging research issue. The high uncertainty of human mobility and limited clue provided by the unfinished trajectory are two major barriers to developing effective predictors. In general, such a prediction task is often converted to the identification one among given candidate destinations. Hence, how to extract the discriminative knowledge from the partial trajectory becomes crucial. It is well-recognized that the sequence of visited locations by the taxi has inherent relationship with the heading destination. Inspired by the idea, we propose a novel approach that jointly combines the GPS embedding and attention-based BiLSTM techniques for the prediction of passenger's destination. Specifically, we propose two GPS embedding methods to encode the geographic proximity and multi-scale spatiality of GPS points into embedding vectors, so as to reveal the spatial context of visited locations in the urban space. After converting GPS trajectories into embedding sequences, we further establish an attention-based dual BiLSTMs neural network to model the relationship between the heading destination and the bidirectional sequential context of visited locations. Meanwhile, the discriminative capability of visited locations in determining the destination can be captured by the attention mechanism. In addition, the OT (origin and time) information is aggregated into the neural network as auxiliary features. Stepping closer to smarter passenger services, rather than telling destinations in terms of drop-off clusters, our proposed model outputs the destinations in terms of historical passengers' destination clusters. Finally, we evaluate the system performance based on two real large-scale datasets. Results show the superior performance of our proposed model.

Mining top-N high-utility operation patterns for taxi drivers

In recent years, the rapid development of mobile network and wireless sensor technology has brought opportunities to change the way of the existing taxi business operation. How to improve the operation revenues of taxi drivers has become a topic worthy of research. This paper analyzes and mines taxi operation data to provide taxi drivers with personalized sequence recommendation services, thereby increasing their expected revenues. Different from previous works, the proposed method in this paper recommends a series of future operation orders for taxi drivers, instead of recommending several discrete locations for the current order. In this paper, firstly, by performing spatial-temporal clustering on the origins and destinations of passengers, the spatial and temporal distribution characteristics of passengers in the city are identified. Secondly, the origin of the current passenger is used as the root node to construct a top-N high-utility sequence tree, and this process can be divided into two processes: top-down building tree and bottom-up sorting path utility. The two pruning strategies of node utility and path utility are used to reduce the generation of candidate sets. Finally, a series of potential orders based on dynamic context are recommended to taxi drivers, so as to maximize the expected revenues of taxi drivers. The experimental results demonstrate that there is a close relationship between taxi drivers’ operation behavior patterns and their revenues. The proposed system framework and algorithm in this paper can effectively mine global and long-term top-N high-utility operation patterns.

Smart System for Preventing Passenger Destination Missing in Bus

Passengers need not to worry any more about the arrival of their destination missing in Bus. Bus passengers will be able to recognize their destination during the journey by using this proposed setup. This project is very useful to the people who are illiterates and new to cities. It also announces important places such as hospitals, police station etc. along with the passenger’s destination. It is one of the parts of Intelligent Transport System which will reduce the workload of Bus drivers and Conductors with the help of automatic station display setup. This project also ensures the safety of bus drivers. In this project we are proposing the design of an embedded device by using the GPS module, which keeps tracking on the bus route and when the destination of the passenger arrives, it is intimated to the passenger with the help of loudspeaker and LCD display. It is one of the most cost effective methods which can be easily implemented by public bus transportation. All kind of people gets benefit by this project

Practical model with strong interpretability and predictability: An explanatory model for individuals' destination prediction considering personal and crowd travel behavior

AbstractReal‐time individuals' destination prediction is of great significance for real‐time user tracking, service recommendation and other related applications. Traditional technology mainly used statistical methods based on the travel patterns mined from personal history travel data. However, it is not clear how to predict the destinations of individuals with only limited personal historical data. In this paper, taking the public transportation metro systems as example, we design a practical method called practical model with strong interpretability and predictability to predict each passenger's destination. Our main novelties are two aspects: (1) We propose to predict individuals' destination by combining personal and crowd behavior under certain context. (2) An explanatory model combining discrete choice model and neural network model is proposed to predict individuals' stochastic trip's destination, which can be applied to other transportation analysis scenarios about individuals' choice behavior such as travel mode choice or route choice. We validate our method based on extensive experiments, using smart card data collected by automatic fare collection system and weather data in Shenzhen, China. The experimental results demonstrate that our approach can achieve better performance than other baselines in terms of prediction accuracy.

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.

Perancangan Aplikasi Notifikasi Jadwal Penerbangan Berbasis Mobile

Penelitian ini bertujuan untuk merancang pengembangan sistem informasi jadwal penerbangan yang memberikan informasi waktu yang sesungguhnya berdasarkan keberadaan pesawat dalam penerbangan kepada pengguna, dan menghasilkan sebuah sistem informasi jadwal penerbangan yang real time sehingga dapat memberikan informasi waktu kadatangan pesawat secara tepat.
 Dalam perancangan pengembangan sistem informasi jadwal penerbangan menggunakan UML Univ (data flow diagram) dan perancangan aplikasinya menggunakan teknologi mobile dengan pemanfaatan web services/API untuk mengakses informasi jadwal penerbangan dan informasi cuaca. Jadwal pererbangan berupa file json yang diperoleh dari website FIDS bandara hasanuddin Makassar, sedangkan informasi cuaca bandara asal dan bandara tujuan penumpang diperoleh dari API Open Wheater.

Detailed Origin-Destination Matrices of Bus Passengers Using Radio Frequency Identification

Origin-destination (OD) matrices provide significant information to optimize the operation of Public Transportation System and design its evolution. The survey-based approaches to collecting data were overcome by technology providing passengers’ data. Check-In/Check-Out methods generate complete information, but obstructing the alighting and impacting safety. Check-In/Not-Check-Out systems do not detect the destination of passengers, resulting in poor estimates. Be-In/Be-Out (BIBO) approaches seem optimal. We present a complete BIBO system that uses Radio Frequency Identification for passenger monitoring. We first formalize the mathematical framework of the system and propose the set of algorithms that calculate the individual journey of each passenger, the corresponding OD matrices, and a set of operation vectors revealing behaviors at each stop and stretch. We describe a novel visualization tool of OD matrices based on bipartite graphs. We carried out two experiments on a bus in real operation. The obtained results show very high performance metrics. We achieve individual journey estimation rates of 94.7% and 87.9% and error rates of 0.0% and 10.5% and high determination coefficients for OD matrices and operation vectors. Consequently, we can state that our system is capable of producing reliable OD matrices representing the detailed mobility of the Public Transportation Services.

Improvement of Short-distance Subsidy Policy for Airport Taxi Based on Cluster Analysis

The income of airport taxis is related to the mileage. Due to the difference in passenger destinations, the mileage is long and short, and the taxi driver cannot pick or refuse to load. Therefore, the difference between short-haul passengers and long-haul passengers is significant. Based on such realistic problems, a relatively reasonable priority policy has been proposed to balance the income of taxi drivers. Under the existing policy based on the short-distance ticket of Shanghai Pudong Airport, combined with the large number of drop-off point data of taxis, the data is pre-processed and then visually analyzed on Bigemap, and then cluster analysis is carried out in matlab environment. The mileage distribution makes a more reasonable division of short-distance long-distance definition and proposes new ideas for solving the problem.

Last train scheduling for maximizing passenger destination reachability in urban rail transit networks

As urban rail transit (URT) systems usually do not operate for the whole day, the last train service offers the last daily chance for late-night passengers to utilize URT services to reach their target destination stations. This paper formally introduces and models the destination-reachability based last train timetabling problem (DR-LTTP in abbreviation) in URT networks, which involves both the last train timetabling and the passenger assignment. The DR-LTTP is formulated as a mixed integer linear programming and can be solved by existing commercial optimization software. The model is illustrated with a simple numerical example on a minimum spanning tree network, and comparison experiments are conducted between DR-LTTP model and station-transferability based last train timetabling problem (ST-LTTP in abbreviation). Finally, a real case study with Beijing URT network is conducted to test the performance of our model.

Estimasi Tujuan Penumpang Menggunakan Predictive Model dengan Data Smart Card

Bus Rapid Transit (BRT) is one of the main choices of public transportation that supports mobility of Jakarta community. As one of the main choices of public transportation, BRT should provide good service and always improve its performance. Needs for moving or mobility will cause a problem if the moving itself is heading at the same area and at the same time. That will cause some problems which are often faced in urban areas such as traffic and delay. To overcome those problems there needs to be a strategy to build good public transportation planning, besides need to know individual travel patterns to overcome problems and improve BRT service. In case to realize those plans needs to be built origin-destination (O-D) matrix. O-D matrix is a matrix that each cell is an amount of trip from the source(row) to the destination (column). O-D matrix is beneficial for analysis, design and public transportation management. O-D matrix also provides useful information like amount of trip between 2 different locations, that can be utilized as fundamental information for decision making for three levels of strategic management (long term planning), tactic (service adjustment and network development), and operational (scheduling, passenger statistic, and performance indicator). To build O-D matrix is required a predictive model that can be measured to predict passenger destination. The predictive model will be build using classification algorithms such as Decision Tree and K-Nearest Neighbor (KNN).

Agent-based evacuation simulation from subway train and platform

With many people using subways at the same time, subway stations must be appropriately designed to allow people to move smoothly and safely during daily operation and emergency evacuation. The present study carries out an agent-based modeling (ABM) of people from trains and platforms in a typical subway station in normal and evacuation scenarios. Developed based on the NetLogo platform, the proposed model enables defining normal and emergency scenarios with realistic considerations of some critical parameters, including number of trains; number of passengers, stairs, ticket gates, exits; passenger destination choices; and possible delaying of exiting the train and station. Comparative studies of normal and emergency situations are conducted on a prototype subway station. Parametric investigations show different impacts of some key parameters on evacuation time and delay and provide some valuable insights for understanding the potential causes of delay of evacuations and possible improvements of the subway station design in terms of emergency response. By adopting site- and event-specific parameters of the station and emergency event, future customized analyses can be conducted based on the proposed model to help design and implement more optimized and tailored emergency response plans and srategies.

Time integration analysis of Soekarno-Hatta International Airport (SHIA) Train with Skytrain

The sustainability of transportation systems in Jakarta is under threat from climate change. Better integration and planning of public transportation is an alternative to support land transportation and to solve the congestion problem when heading to Soekarno-Hatta International Airport. Hence, PT Railink, PT Angkasa Pura II, PT Kereta Api Indonesia with its BUMN synergy built rail-based transportation called Airport Train. Moreover, Airport Train is connected with Skytrain in an integrated building as a transferring point. This study analyzes the time integration to travel using the Airport Train from BNI City Station to the Skytrain to reach the passenger destination terminal by observing additional time factors in the integration building or vice versa on the Skytrain – Airport Train trips. The results of the analysis in the form of Train Travel Charts show both on Airport Train - Skytrain trip and Skytrain - Airport Train trip after paying attention to integration factors, there are several schedule conditions that require passengers to depart for the next Airport Train or Skytrain, and worse not accommodate each other at certain times.