User Travel Research Articles

Analysis of an Autonomous Driving Modular Bus System

One of the main complaints about the performance of the bus systems is the time lost at recurrent stops along the route, that results in a low bus speed service. Among the potential measures to speed up the bus service, the introduction of the Autonomous Driving Modular Bus, able to adjust the transport unit by coupling or decoupled pods, will represent a new paradigm in the provision of service. This concept is able to maintain high stable cruising speeds to the transport unit, decoupling only one pod with passengers that may alight at the next stop. This paper analyze the potentialities of this concept in terms of user travel times and operationg cost. This work also features the formulation and analytical models to calculate the performance and oeprational variables, implementing them into two existing bus lines in Barcelona.

Optimization Model of Network‐Level Pavement Maintenance Decision considering User Travel Time and Vehicle Fuel Consumption Costs

The maintenance management decisions of network‐level asphalt pavements have long been a challenge to highway agencies, and a great amount of factors have been involved. In this study, a network‐level optimization method was established by integrating the maintenance benefits into the zero‐one programming optimization model. An optimized performance evaluation method of asphalt pavement was proposed which contains 11 different kinds of combinations. The benefit model quantifies the cost savings of user travel time and vehicle fuel consumption to the pavement condition index (PCI) and ride quality index (RQI), respectively. Based on the simplified evaluation method as well as the quantified maintenance benefit model, an optimization model was established by employing the zero‐one programming. This optimization model aimed to maximize the improvements/price ratio of pavement maintenance for the whole pavement network. The calculation results present the optimal strategies of maintenance for every road section in the network. The applicability of the newly proposed model was validated by a case study. The methodology developed in this study helps to offer guidelines to highway agencies in managing and making decisions about network‐level pavement maintenance.

Investigating the factors affecting the distance travel and health conditions of e-bike users in Toronto

This paper investigates the factors influencing the physical health condition and the trip distance of e-bike users’ in Toronto, Canada. The research is based on a survey of e-bike and bicycle users' health condition and travel behaviour in Toronto. A Bivariate Ordered Probit model is used to draw links between different factors that affect the health conditions and trip distance of the users. It is important to note that modelling results at best indicate an association (not causation) between the dependent and independent variables. The health condition model reveals that e-bikers from low-income households have higher health risk than the e-bikers from the high-income household. In terms of the trip distance, the model results indicate that the individuals who typically make longer distance trips using e-bikes are more likely to be in an excellent health condition than the individuals who make shorter distance trips using e-bikes. In terms of policy recommendations, a Monte Carlo analysis based on the estimated model reveals that strategies targeted at different demographics (e.g., income, age) are more likely to be effective. The implications of these empirical findings provide essential elements of guidance to the policymakers, and these findings are transferable to the other North American cities.

Key barriers in MaaS development and implementation: Lessons learned from testing Corporate MaaS (CMaaS)

To reach the full potential of Mobility as a Service (MaaS), especially its projected positive environmental impacts, the barriers to development and implementation processes must be identified. However, studies identifying such MaaS barriers are rare. Following an interdisciplinary approach, this paper aims to bridge this gap by adding knowledge on barriers to MaaS development and implementation using four perspectives (service design, business model, user travel attitude and behavior, and system impacts). Following a systems thinking approach, the barriers are investigated at three levels (individual, organizational and societal) to show their relationships. This paper investigates a specific type of MaaS, namely Corporate Mobility as a Service (CMaaS). The results obtained by investigating a large-scale CMaaS pilot provide implications of general barriers to MaaS development and implementation. The findings presented in this paper provide knowledge and guidance to MaaS stakeholders.

NoDOS(3) Calling!! Propuesta de trazos, huellas y recorridos como resultado de una residencia artística online

En el presente fotoensayo se comparte un proyecto artístico intermedia, realizado en The Spectacular House, una plataforma de divulgación de arte y pensamiento, comisariada por Sofia Caetano y establecida en Pittsburgh (USA) y Ponta Delgada (Azores). Su labor se muestra en una página web creada con el objetivo de promover el arte y la cultura de manera virtual a escala global. Dentro de esta plataforma, se encuentra “Gate”, una puerta que se abre hacia una galería de arte online donde se alojan proyectos site specific y proyectos generados en Internet como parte del proceso desarrollado en un entorno de residencia artística online. Y es dentro de este contexto donde el Grupo NoDOS(3), formado por Cristina Ghetti y Elia Torrecilla, desarrolló un proyecto durante dos semanas. El resultado final es una pieza digital, con una fuerte presencia de lo sonoro, donde las interacciones del usuario, las experiencias de desplazamientos, hipervínculos, vídeos, fotos y recorridos múltiples permiten visualizar un trabajo de naturaleza híbrida, de una manera diferente y creativa.
 
 En este fotoensayo, se propone una visualización del proyecto a través de imágenes que muestran nuestro proceso de trabajo y que permiten al lector atravesar la pantalla a través del hipervínculo. De este modo, el visitante podrá participar de las investigaciones que NoDOS(3) llevamos a cabo, y que están basadas en el uso del cuerpo y la tecnología en el espacio (físico, virtual e híbrido) a través de la performance. En definitiva, ofrecemos la posibilidad de que el visitante realice un recorrido de manera lúdica e interactiva a través de un fotoensayo a golpe de “click”.

Continuously monitoring alternative shortest paths on road networks

Modern navigation systems do not only provide shortest paths but also some alternative paths to provide more options to the users. This paper is the first to study the problem of continuously reporting alternative paths for a user traveling along a given path. Specifically, given a path P on which a user is traveling, we continuously report to the user k paths from the user's current location on P to the target t. We present several algorithms each improving on the previous based on non-trivial observations and novel optimisations. The proposed algorithms maintain and exploit the previously computed useful information to efficiently update the k alternative paths as the user moves. We provide space and time complexity analysis for each proposed algorithm. We conduct a comprehensive experimental study on large real-world road networks. The results demonstrate that the proposed algorithms are up to several orders of magnitude faster than the straightforward algorithms.

A Context-Aware Location Recommendation System for Tourists Using Hierarchical LSTM Model

The significance of contextual data has been recognized by analysts and specialists in numerous disciplines such as customization, data recovery, ubiquitous and versatile processing, information mining, and management. While a generous research has just been performed in the zone of recommender frameworks, by far most of the existing approaches center on prescribing the most relevant items to customers. It usually neglects extra-contextual information, for example time, area, climate or the popularity of different locations. Therefore, we proposed a deep long-short term memory (LSTM) based context-enriched hierarchical model. This proposed model had two levels of hierarchy and each level comprised of a deep LSTM network. In each level, the task of the LSTM was different. At the first level, LSTM learned from user travel history and predicted the next location probabilities. A contextual learning unit was active between these two levels. This unit extracted maximum possible contexts related to a location, the user and its environment such as weather, climate and risks. This unit also estimated other effective parameters such as the popularity of a location. To avoid feature congestion, XGBoost was used to rank feature importance. The features with no importance were discarded. At the second level, another LSTM framework was used to learn these contextual features embedded with location probabilities and resulted into top ranked places. The performance of the proposed approach was elevated with an accuracy of 97.2%, followed by gated recurrent unit (GRU) (96.4%) and then Bidirectional LSTM (94.2%). We also performed experiments to find the optimal size of travel history for effective recommendations.

Research on power-law distribution of long-tail data and its application to tourism recommendation

PurposeOne challenge for tourism recommendation systems (TRSs) is the long-tail phenomenon of ratings or popularity among tourist products. This paper aims to improve the diversity and efficiency of TRSs utilizing the power-law distribution of long-tail data.Design/methodology/approachUsing Sina Weibo check-in data for example, this paper demonstrates that the long-tail phenomenon exists in user travel behaviors and fits the long-tail travel data with power-law distribution. To solve data sparsity in the long-tail part and increase recommendation diversity of TRSs, the paper proposes a collaborative filtering (CF) recommendation algorithm combining with power-law distribution. Furthermore, by combining power-law distribution with locality sensitive hashing (LSH), the paper optimizes user similarity calculation to improve the calculation efficiency of TRSs.FindingsThe comparison experiments show that the proposed algorithm greatly improves the recommendation diversity and calculation efficiency while maintaining high precision and recall of recommendation, providing basis for further dynamic recommendation.Originality/valueTRSs provide a better solution to the problem of information overload in the tourism field. However, based on the historical travel data over the whole population, most current TRSs tend to recommend hot and similar spots to users, lacking in diversity and failing to provide personalized recommendations. Meanwhile, the large high-dimensional sparse data in online social networks (OSNs) brings huge computational cost when calculating user similarity with traditional CF algorithms. In this paper, by integrating the power-law distribution of travel data and tourism recommendation technology, the authors’ work solves the problem existing in traditional TRSs that recommendation results are overly narrow and lack in serendipity, and provides users with a wider range of choices and hence improves user experience in TRSs. Meanwhile, utilizing locality sensitive hash functions, the authors’ work hashes users from high-dimensional vectors to one-dimensional integers and maps similar users into the same buckets, which realizes fast nearest neighbors search in high-dimensional space and solves the extreme sparsity problem of high dimensional travel data. Furthermore, applying the hashing results to user similarity calculation, the paper greatly reduces computational complexity and improves calculation efficiency of TRSs, which reduces the system load and enables TRSs to provide effective and timely recommendations for users.

Micromobility evolution and expansion: Understanding how docked and dockless bikesharing models complement and compete – A case study of San Francisco

Shared micromobility – the shared use of bicycles, scooters, or other low-speed modes – is an innovative transportation strategy growing across the United States that includes various service models such as docked, dockless, and e-bike service models. This research focuses on understanding how docked bikesharing and dockless e-bikesharing models complement and compete with respect to user travel behaviors. To inform our analysis, we used two datasets from February 2018 of Ford GoBike (docked) and JUMP (dockless electric) bikesharing trips in San Francisco. We employed three methodological approaches: 1) travel behavior analysis, 2) discrete choice analysis with a destination choice model, and 3) geospatial suitability analysis based on the Spatial Temporal Economic Physiological Social (STEPS) to Transportation Equity framework. We found that dockless e-bikesharing trips were longer in distance and duration than docked trips. The average JUMP trip was about a third longer in distance and about twice as long in duration than the average GoBike trip. JUMP users were far less sensitive to estimated total elevation gain than were GoBike users, making trips with total elevation gain about three times larger than those of GoBike users, on average. The JUMP system achieved greater usage rates than GoBike, with 0.8 more daily trips per bike and 2.3 more miles traveled on each bike per day, on average. The destination choice model results suggest that JUMP users traveled to lower-density destinations, and GoBike users were largely traveling to dense employment areas. Bike rack density was a significant positive factor for JUMP users. The location of GoBike docking stations may attract users and/or be well-placed to the destination preferences of users. The STEPS-based bikeability analysis revealed opportunities for the expansion of both bikesharing systems in areas of the city where high-job density and bike facility availability converge with older resident populations.

Electric vehicle car-sharing optimization relocation model combining user relocation and staff relocation

ABSTRACT Factors including unbalanced user travel demand, vehicle charging status, and high operating cost of enterprises have restricted the development of electric vehicle car-sharing. This paper comprehensively considers the impact of multiple dynamic constraints such as user demand, state of charge of electric vehicles, and operating profit on vehicle relocation. The travel behavior of consumers is studied through Multinomial Logistic Regression method. User demand for electric vehicle car-sharing is forecast through Hidden Markov Model. A new vehicle relocation strategy combining staff relocation and user relocation is formulated. With the goal of maximizing enterprise profit, an electric vehicle car-sharing optimization relocation model in region level is finally established. Taking Anting Town as an example to verify the model, the results show that this new vehicle relocation strategy can effectively reduce the operating cost of enterprises, improve the circulation rate and utilization rate of vehicles, and reduce unnecessary waste of resources.

Communication with Self-Growing Character to Develop Physically Growing Robot Toy Agent

Robots for communication are developed extensively with an emphasis on sympathy. This study deals with the growth of character and the control of its operation. The child has time to be alone with the nature of his/her robot friend. That child can interact with other people’s emotional expressions through a robot. Step by step, the robot character will grow as the child grows. Through design studies, qualitative processes such as customer experience audit, eye tracking, mental model diagrams, and semantic differences have been executed for the results. The participatory behavior research approach through user travel is mapped from the user’s lead to the evidence-based design. This research considers how synthetic characteristics can be applied to the physical growth of robot toys through the product design process. With the development of robot toy “Buddy”, two variations on the robot were made to achieve recognizable growth. (1) one-dimensional height scaling and (2) facial expression including the distance between two eyes on the screen. Observations represented children’s reactions when "Buddy" was released with the children. As an independent synthetic character, the robot was recognized by children who had the designed function. Robots for training may require more experimentation.

A Combined Modal Split and Traffic Assignment Model With Capacity Constraints for Siting Remote Park-and-Ride Facilities

From the perspective of urbanization, commuters' long-distance and diversified trips are suitable for the connection between city and suburbs. The existing park-and-ride (P&R) facilities can influence user travel decisions, promoting the usage of public transportation and reducing the environmental pollution, but the P&R lots are usually located near train stations that the construction of lots at these locations is costly and complex. It is important to analyze the need for P&R and its capacity before its construction. Therefore, this paper proposes a bi-level model for determination of optimal location and capacity of a remote P&R (RPR) facility that allows a user to park a vehicle in a suburban area at lower cost and then take a bus to the nearby train station. A combined modal split and traffic assignment model with capacity constraints is developed as a lower-level model, and a nested-logit model is adopted to manage the mode similarity. The upper-level optimization is conducted based on the generalized cost of the transportation system according to the budget and environmental limits. The performance of the proposed model is experimentally verified that the optimal RPR scheme shifts commuters from the automobile mode to the transit and RPR modes, thus improving the social benefit dramatically. The model and analysis provide insights that may help operators determine how RPR facilities can be established to meet social cost improvement goals.

Augmented Intention Model for Next-Location Prediction from Graphical Trajectory Context

Human trajectory prediction is an essential task for various applications such as travel recommendation, location-sensitive advertisement, and traffic planning. Most existing approaches are sequential-model based and produce a prediction by mining behavior patterns. However, the effectiveness of pattern-based methods is not as good as expected in real-life conditions, such as data sparse or data missing. Moreover, due to the technical limitations of sensors or the traffic situation at the given time, people going to the same place may produce different trajectories. Even for people traveling along the same route, the observed transit records are not exactly the same. Therefore trajectories are always diverse, and extracting user intention from trajectories is difficult. In this paper, we propose an augmented-intention recurrent neural network (AI-RNN) model to predict locations in diverse trajectories. We first propose three strategies to generate graph structures to demonstrate travel context and then leverage graph convolutional networks to augment user travel intentions under graph view. Finally, we use gated recurrent units with augmented node vectors to predict human trajectories. We experiment with two representative real-life datasets and evaluate the performance of the proposed model by comparing its results with those of other state-of-the-art models. The results demonstrate that the AI-RNN model outperforms other methods in terms of top-k accuracy, especially in scenarios with low similarity.

Adaptive Group-Based Zero Knowledge Proof-Authentication Protocol in Vehicular Ad Hoc Networks

Vehicular Ad Hoc Networks (VANETs) are a particular subclass of mobile ad hoc networks that raise a number of security challenges, notably from the way users authenticate the network. Authentication technologies based on existing security policies and access control rules in such networks assume full trust on Roadside Unit (RSU) and authentication servers. The disclosure of authentication parameters enables user's trace-ability over the network. VANETs' trusted entities (e.g. RSU) can utilize such information to track a user traveling behavior, violating user privacy and anonymity. In this paper, we proposed a novel, light-weight, Adaptive Group-based Zero Knowledge Proof-Authentication Protocol (AGZKP-AP) for VANETs. The proposed authentication protocol is capable of offering various levels of users' privacy settings based on the type of services available on such networks. Our scheme is based on the Zero-Knowledge-Proof (ZKP) crypto approach with the support of trade-off options. Users have the option to make critical decisions on the level of privacy and the amount of resources usage they prefer such as short system response time versus the number of private information disclosures. Furthermore, AGZKP-AP is incorporated with a distributed privilege control and revoking mechanism that render user's private information to law enforcement in case of a traffic violation.

An Interactive Strategy of Grid and Electric Vehicles Based on Master-Slaves Game Model

In order to avoid the adverse effects of the grid caused by disorder large-scale electric vehicles and improve supporting function of energy storage battery on the power grid, interaction model of electric vehicles (EVs) and the power grid is constructed according to a master-slave game method. Then, the utility functions of each participant are established, and the dynamic constraints of the batteries for EVs are established considering the travel demand of users. Then, based on the YALMIP/CPLEX solver, the optimal response strategy of the EVs can be obtained, and the genetic algorithm is used to solve the Nash equilibrium solution of the master-slaves game model. Moreover, the Monte Carlo method is employed to simulate the user travel data and the basic parameters of EVs in a certain area. Based on these data, the optimal time-of-use price in this area is offered, and the optimal response strategy of EVs under this price is analyzed. Finally, the study of the case show that the proposed game model can make the strategies of both the EVs and the grid, which can restrict the load fluctuation of the grid and improve the benefit of EVs without affecting their travel.

The Impact of Go-food on Travel Behaviour for The Fulfillment of Consumption Needs in Higher Education Areas

This study examines the Go-food implications for changing the behavior of people’s travels in fulfilling consumption needs, specifically for eating and drinking. This community travel behaviour is seen from two sides, namely: Go-food user request side and provider side (Go-food driver travel intensity). Quantitative methods were applied to analyse Go-food implications towards travel behaviour from provider and user. The results of this study explain that a high level of travel occurs at night and is hostile to the beginning of the driver’s travel. It is influenced by the function of the trade area that is located on the surrounding road, especially when weekdays on Sirojudin Street (21%), while the weekend at Ngesrep Timur V Street (20%) and Jatimulyo Street (17%). In addition, the behaviour of the driver’s travel goals is spread, which is when weekdays in the Bulusan area (30%), while in the weekend in the Pedalangan area (27%). Fulfillment of this consumption needs affects the change in Go-food user travel behaviour. Before there was Go-food, several factors were considered, namely: distance, time, cost, frequency, and reason for travel, whereas after Go-food, factors that are considered or have a relationship are: frequency factor and goal travel. This is because the Go-food service is used when the community prioritizes its time for productive matters. Moreover, the Go-food facility resulted in the Go-food user making a request to the place where he wanted to eat, although the distance was quite far. This is because cost and distance are not a problem for Go-food consumers.

Railway Infrastructure and Traveller usage Prediction and Rendering Solutions

This project introduces the primary establishments of Big Data connected to Smart Cities. An IOT based mechanism is proposed to be connected to various areas. In this project, we are trying to predict and provide the solution to improvise the railway / bus infrastructure and their services. Indian local & state railways or buses are a mode of transport service where thousands of people process every minute. Thus our proposed system involves data collection of the users based on id, username, gender, age, the timing of travel, station source and destination to monitor the user travel behavior. Thus the collected data can be used for analytics and prediction. Predicting the consumer's count and behavior who uses the railway services are solved through the R Programming. The data analytics are performed using R studio. For this work, In R programming, we use K-means algorithm for clustering and use Naive Bayes algorithm for machine learning and solution defining. Finally, the predictive output is sent for public access using shinyapps.io. These results are useful to the travelling systems for giving better services to passengers.

Pick-me: A Carpooling Clustering System for Alternative Transportation

Many travel systems were built to facilitate user travel experience while these systems flourish in some cases, it fails in other cases as explained in [5]. Pick-me is a clustering system that provides an alternative travel method on a regular basis and cheaper than the previous solutions. The system aims to reduce traffic and fuel consumption by minimizing the number of vehicles and maximizing the number of passengers per vehicle. The system introduces a new algorithm Dynamic Distance Minimizer, the algorithm chooses the points of interest through a given route and decides based on learning patterns the best route. The system has proven its efficiency by testing it on 16 routes with a Recommendation accuracy of 98%.

Railway Infrastructure and Traveller usage Prediction and Rendering Solutions

This project introduces the primary establishments of Big Data connected to Smart Cities. An IOT based mechanism is proposed to be connected to various areas. In this project, we are trying to predict and provide the solution to improvise the railway / bus infrastructure and their services. Indian local & state railways or buses are a mode of transport service where thousands of people process every minute. Thus our proposed system involves data collection of the users based on id, username, gender, age, the timing of travel, station source and destination to monitor the user travel behavior. Thus the collected data can be used for analytics and prediction. Predicting the consumer's count and behavior who uses the railway services are solved through the R Programming. The data analytics are performed using R studio. For this work, In R programming, we use K-means algorithm for clustering and use Naive Bayes algorithm for machine learning and solution defining. Finally, the predictive output is sent for public access using shinyapps.io. These results are useful to the travelling systems for giving better services to passengers.

Effective social-context based message delivery using ChitChat in sparse delay tolerant networks

Delay tolerant networks (DTNs) have garnered much interest with the wide-spread adoption of portable smart devices capable of wirelessly connecting with one another, thus enabling the formation of a network for opportunistic data dissemination. This type of network is useful in a variety of applications where other form of network communication strategies are unavailable, such as an on-the-ground tactical military network in an active battlefield or an emergency network formed immediately after a catastrophic disaster. DTNs also provide opportunities for various other interesting applications such as location-based social networking, interests-based data dissemination, and geolocal advertising. One persistent challenge for DTNs is achieving sufficient message delivery due to the dynamic, unpredictable, and opportunistic nature of inter-device connections; this challenge is exacerbated when such connections are sparsely available. In this paper, a novel social-context based message routing system, called ChitChat, is proposed with the focus on message delivery through sparsely-connected DTNs. ChitChat is a hybrid geographic/data-centric routing system designed to exploit each user’s social (or mission) interests to opportunistically learn of multi-hop paths through the network, and to derive the social semantics of geographic locations using user travel itineraries and multi-hop social relationships. In turn, this information is used to make distributed routing decisions based on the likelihood an encountered node will connect with others capable of successfully delivering a message. An analysis of network sparsity is conducted against five real-world datasets. Through simulations using the two highest-sparsity real-world datasets, ChitChat is capable of achieving more successful deliveries against three recent state-of-the-art DTN routing schemes while incurring lower costs against flooding.