Trip Planning Research Articles

Whose express access? Assessing the equity implications of bus express routes in Montreal, Canada

Express buses—characterized by limited stops and sometimes higher frequencies or priority traffic measures—offer a cost-effective and efficient way to boost service convenience and reliability for riders. This paper assesses how the accessibility benefits of express bus route policy are distributed in Montreal, Canada, while providing a pathway for public transportation agencies to assess their policies and plans. To isolate the impact of bus express routes, we use General Transit Speed Specification (GTFS) data, the Open Trip Planner multimodal routing engine, and the 2013 edition of Montreal’s origin-destination survey to contrast travel time and accessibility at the trip and census-tract levels under two scenarios: one with the existing, complete network and the second a counterfactual scenario with no express bus routes. Our results indicate that bus express routes enable an overall increase in accessibility for the overall population. However, the accessibility benefits do not accrue evenly, as expected, but also tend to benefit a more significant number of higher incomes. This occurs despite the location of low-income populations in some outlying areas of the city, which express bus routes are supposed to serve. This paper closes with policy recommendations that help planners balance economic, environmental, and equity goals, perhaps one of the most complex challenges they face nowadays.

Configuration approach for personalized travel mashup

AbstractNowadays, we live in an interconnected ecosystem of heterogeneous services which are available anytime and anywhere. Although these services can be used individually, their combined usage provides end‐users with real added value. Nevertheless, existing mashup solutions are not fully targeted to end‐users. Indeed, these solutions require technical knowledge to deal with the technological heterogeneity in which they are offered. The key limitation of current mashup solutions is their general‐purpose nature, which privileges expressive power over intuitiveness. As the reason for mashups was the desire of end‐users to build applications responding to their situational needs, we propose a domain‐specific mashup approach that “speaks the language of the user.” The article presents (1) a domain‐specific visual language that allows end‐users to easily build mashup‐based compositions, (2) a mashup schema which is a gateway between service implementations and end‐users, providing a high‐level description in order to hide technological issues when including services in composition; and (3) a configuration module that executes the travel mashup query and generates a personalized trip planning. The results of a user study confirm that domain‐specific mashup tools lower the entry barrier to mashup development.

Multi-Objective Trip Planning With Solution Ranking Based on User Preference and Restaurant Selection

The tourist trip design problem (TTDP) helps the trip planners, such as tourists, tour companies, and government agencies, automate their trip planning. TTDP solver chooses and sequences an optimal subset of point of interest (POIs), which adhere to the POIs attributes and tourist preferences, and then generates a travel itinerary that maximizes their pleasure. However, the traditional TTDP does not include the lunch period at a local restaurant, which causes the rest of the itinerary in the afternoon to shift, nor compulsory POIs that the trip planners must be included in the itinerary. Moreover, as tourism contributes to high greenhouse gas emissions, especially from its transportation, minimizing the itinerary’s total distance is also considered. Unfortunately, this objective conflicts with the profit scores; no single itinerary can optimize both objectives simultaneously. Hence, the multi-objective technique and the results of non-dominated itineraries can be organized as a Pareto front. The trip planners can choose one suitable itinerary from the Pareto front based on their preferences. To address these real-world issues, we formulate a new variant of the well-known orienteering problem with time windows (OPTW) called the multi-objective orienteering problem with Time Windows, Restaurant Selection, and Compulsory POIs (MOPTW-RSCP). The proposed problem is provided with a mathematical formulation and two exact algorithms for solving them, i.e., greedy and branch-and-cut Pareto-based techniques. The algorithms’ performance is tested against the Rattanakosin island (the old city of Bangkok) dataset. We conduct 24 test cases, and the computational results confirm the algorithms’ efficiency.

Multi-Objective Trip Planning Based on Ant Colony Optimization Utilizing Trip Records

Trip planning services have been developed along with tourism promotion and information technology evolutions, where we must construct trip routes that simultaneously optimize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-objective</i> functions such as trip expenses and user satisfaction. Moreover, utilization of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">past-trip records</i> is essential, because similarities to past-trip records well reflect users’ general preferences and tendencies during trip planning. In this paper, we propose a multi-objective trip planning method using ant colony optimization (ACO). By effectively using the pheromones in ACO, we can construct trip routes similar to trip records stored before and the constructed route can reflect users’ general preferences. In addition, we vary ants’ behaviors in ACO corresponding to various objective functions and hence we can obtain multi-objective trip routes naturally. Experimental results demonstrated that our method outperforms the baseline methods in terms of point-of-interest (POI) satisfaction, POI cost, and past-trip similarity. We also conducted a user study, which clearly indicates that our method obtains high scores through various user questionnaires.

Exploring relevant factors behind a MaaS scheme

Mobility as a service (MaaS) is a newly emerging concept that is mainly focused on user mobility needs. MaaS is expected to reshape the way people travel and improve future urban mobility. However, research shows that there is neither a consensus on an unequivocal MaaS definition nor a universal best way to evaluate and compare MaaS schemes. Thus, given the large differences between MaaS packages, this research provides new insights into what is offered in a variety of MaaS platforms with special concerns related to functionalities and information types, customisation, and possible integration of specific societal goals. For that purpose, various European MaaS providers/platforms were explored under the PriMaaS partnership, composed of heterogeneous regions. Data were aggregated and analysed applying cross-tabulation and clustering techniques. The proposed approach led to relevant factors, such as Payment, Customisation, and Trip Planning. Relationships between platform characteristics and societal goals suggest these can be used as a conceptual foundation. Our study provides a baseline for establishing a conceptual framework to develop sustainable MaaS indicators, which would help citizens using MaaS more efficiently and support MaaS providers and operators in developing sustainable business models.

Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

Recent advancements in cloud computing (CC) technologies signified that several distinct web services are presently developed and exist at the cloud data centre. Currently, web service composition gains maximum attention among researchers due to its significance in real-time applications. Quality of Service (QoS) aware service composition concerned regarding the election of candidate services with the maximization of the whole QoS. But these models have failed to handle the uncertainties of QoS. The resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by end-users. On the other hand, trip planning is an essential technique in supporting digital map services. It aims to determine a set of location based services (LBS) which cover all client intended activities quantified in the query. But the available web service composition solutions do not consider the complicated spatio-temporal features. For resolving this issue, this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model (F3L-WSCM) in a cloud environment for location awareness. The presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking, hotels, car rentals, etc. At the next stage, the firefly algorithm is applied to generate composition plans to minimize the number of composition plans. Followed by, the fuzzy subtractive clustering (FSC) will select the best composition plan from the available composite plans. Besides, the presented F3L-WSCM model involves four input QoS parameters namely service cost, service availability, service response time, and user rating. An extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy, execution time, and efficiency.

A Multimodal Tourist Trip Planner Integrating Road and Pedestrian Networks

A Multimodal Tourist Trip Planner Integrating Road and Pedestrian Networks

The method of route optimization of electric vehicle

Effective trip planning should consider the location of vehicle charging stations. Optimizing the route of electric vehicles should be carried out taking into account the location of the charging stations. The article presents a proposal of a method for optimizing electric vehicle journeys with the use of genetic algorithms. The method developed by the authors considers the range of the electric vehicle, the location of the charging stations and the parameters of the slow / fast charging station. Initial verification of the method was carried out on the example of a travel route in Poland, taking into account the actual locations of fast and slow charging stations. The criterion adopted was the minimization of the traveled distance.

Analysis of customer sentiments on review sites toward selected travel companies: A big data approach

In India, the travel industry is expanding quickly, and more people are using technology to make online transactions easier. This has caused technology and trip planning to become increasingly entwined. According to studies conducted all around the world, contemporary travellers need interactive features, superior travel assistance, product details and pertinent details to help them make decisions. Because travellers evaluate their experiences based on the impression they have of a place after their journey, online travel reviews are also a good predictor of a location’s post-visit destination image (DI) (González-Rodrguez et al. 2016; Park et al. 2007). The purpose of this study is to find out customers’ experience using travel review sites that impact the travel industry. The advancements in technology have had a significant impact on the travel and tourism sector, as they have on many other service-based businesses. This research paper also examines Indian clients’ preference, usage and satisfaction level with online review sites. In this study, Airbnb, Goibibo, Oyo, Yatra, MakeMyTrip and Cleartrip customer reviews are analyzed using Wordcloud and sentiment analysis, and therefore, conclusions are made based on that. The study concludes that ill-defined situations caused by travel firms are impacting customer satisfaction levels. However, it is concluded that consumers place less satisfaction on travel sites due to various reasons.

Time-Dependent Visiting Trip Planning with Crowd Density Prediction Based on Internet of Things Localization

This paper proposes a time-dependent visiting trip planning (TVTP) framework to find the most efficient visiting order and plan the fastest moving paths based on Internet of Things (IoT) localization. The proposed TVTP framework consists of a deep learning based crowd density prediction model and a time-dependent visiting trip planning algorithm. In the developed prediction model, densely connected convolutional networks are explored with spatiotemporal data fusion to further reduce prediction errors. In the designed planning algorithm, visitors are guided to multiple target places at feasible time points to minimize total moving time based on predicted future crowd densities. According to our review of relevant research, this is the first framework that integrates deep learning for crowd density prediction with time-dependent planning for a multi-target visiting trip, which can precisely estimate future moving times based on predicted crowd densities and efficiently plan the optimal visiting order and guiding paths with the shortest total moving time to visit all target places. The open crowd dataset is adopted to evaluate the performance of existing works and TVTP. Experimental results show that our framework outperforms existing methods and can accurately predict the future crowd density of indoor people as well as significantly reduce the total moving time in the planned multi-target visiting trip.

Joint Optimal Power Source Sizing and Data Collection Trip Planning for Advanced Metering Infrastructure Enabled by Unmanned Aerial Vehicles

The use of unmanned aerial vehicles (UAVs) in the collection of data from wireless devices, sensor nodes, and the Internet of Things (IoT) devices has recently received significant attention. In this paper, we investigate the data collection process from a set of smart meters in advanced metering infrastructure (AMI) enabled by UAVs. The objective is to minimize the total annual cost of the electric utility by jointly optimizing the number of UAVs, their power source sizing, the charging locations as well as the data collection trip planning. This is achieved while considering the energy budgets of batteries of UAVs and the required amount of collected data. The problem is formulated as a mixed-integer nonlinear programming (MIN-LP), which is decoupled into two sub-problems where a candidate UAV and a number of buildings are first grouped into trips via genetic algorithms (GAs), and then the optimum trip path is found using a traveling salesman problem (TSP) branch and bound algorithm. Simulation results show that the battery capacity or the number of UAVs increases as the coverage area or the density increases.

군함과 선교: 1842년 세실·장시니·리브와의 〈조선 구상〉과 顚倒된 실행, 김대건·최양업의 입국과 순교

The “Warship” and the “Bible” were closely connected as symbols of “Western Powers over the East” in the mid-19th century. From the present perspective, the combination of the warships and the mission seems mismatched and wrong, but formerly often praised as “the Devine Providence.” The “Joseon Plan” of the French Admiral J.B. Cécille and the special envoy D. Jancigny, and the favorable response of the priest N. Libois to the plan, encouraged the boarding of J. A. Maistre, Kim Daegoeon and Choi Yangeop on French warship in 1842. However, in the middle of 1842, Cécile and Jancigny withdrew their trip plan to Joseon. This decision was related to French policy of active intervention in China and Vietnam after the signing of Nanjing Treaty. The complete entry of Kim Daegeon, Choi Yangeop, and J. A. Maistre to Joseon took ten year, and none of their entry was made by warship. The belated appearance of Cécille’s warship on the Korean west coast and its premature evacuation in 1846, on the contrary, hastened Kim Daegeon’s death. The basic position of Kim Daegeon and the French priests was to obtain religious freedom in a peaceful way. Although they advocated the usage of warships or the armed demonstration, they did not intend to wage war.

Sentiment Classification of Tourist’s Opinion on Tourist Places of Interest in South India using Tweet Reviews

AbstractThe emergent technology has been increasingly incorporated with the tourism industry. In the digital world, social media plays an important role in identifying the most visited tourist places. More than 80% of people commonly used the social media platform called ‘Twitter’. By using tweet reviews, tourists around the world know about the feelings, suggestions and opinions of most visited tourist places in the selected region. This paper recommends an approach for the development of trip planning in South India based on tourist’s preferences. For the research, we use Twitter Application Programming Interface to collect information about the most visited tourist places in South India by using tweets reviews and store it as South India Tourism Tweet reviews database. This paper mainly focuses on analyzing Twitter reviews, which are very helpful to locate the most visited tourist place. In the tweets where reviews are mostly unstructured and heterogeneous, which are then classified into positive tweets, negative tweets and neutral tweets by using the preprocessing technique and machine learning algorithm called support vector machine. Performance measures are calculated by using the classification results. Finally, the results are plotted by using the classification results and performance measures as a wordcloud. This can be very useful for tourists to select the most visited tourist locations. The proposed system can satisfy individual tourist needs.

Energy Storage Systems for Electric Vehicles [Book News

The book contains 25 carefully selected papers covering new trends in energy storage systems. Internal combustion engine cars are planned to be sidelined by 2035 given that the European Commission recently imposed tougher CO2 emission reduction targets that will effectively ban sales of new diesel and gasoline vehicles beyond 2035. However, present- day electric vehicles (EVs) still give drivers some range and charging point anxiety, needing careful planning of longer trips. Therefore, research, innovation, and education must be enhanced in the field of EV inboard energy storage, and also for off-board storage, increasing the penetration of renewable energy to charge EVs, a requirement for sustainability. The book’s editor, Erik Schaltz, is an associate professor in the Department of Energy Technology, Aalborg University, Denmark. He is the program leader of the research program E-mobility and Industrial Drives and the vice program leader of Battery Storage Systems and has participated in more than 15 national and international research projects. The book is a free-access tool for engineers and researchers in EV energy storage systems. The book is also suited for students willing to further explore energy storage in EVs and is a valuable resource for practicing professionals in need of understanding and pursuing advanced trends in energy storage systems.

Investigating the impact of COVID-19 on user perception for deriving policies and intervention areas for urban bus service in India

The paper aims to investigate the impact of COVID-19 pandemic on the perception of travelers and identify new policies and intervention areas for improving urban bus service. This was carried out based on the perception data collected from travelers in the years 2018 (before pandemic) and 2020(in post lockdown scenario). Data was collected from the state of Kerala, India, and Importance-Satisfaction Analysis (ISA) with fuzzy c-means clustering was carried out to identify improvement areas for both scenarios. Results show a substantial increase in the number of priority areas as compared to the pre-pandemic scenario. Bus service attributes such as cleanliness, crowding level, and pedestrian environment emerged as new intervention areas of bus service during the pandemic. This is a clear reflection of the increased awareness of travelers about the prevailing pandemic scenario and the need for improvement in attributes related to social distancing and hygiene. The higher priority to information related attributes of bus service clearly shows the need for improvement in such attributes for the safe planning of trips during the pandemic. The work also estimated desired service levels of quantitative attributes to enhance the satisfaction levels of users. An evaluation of five bus routes in Kochi based on the desired levels shows the underperformance of attributes such as headway, punctuality, and crowding level. While the findings are city-specific, the methodology and experience may be adopted to enhance the service quality of buses and other public transport.

‘Mobility work’: Older adults' experiences using public transportation

This paper contributes to research on public transport accessibility, disabling spaces, and older adult's mobility by highlighting the ‘mobility work’ older adults complete to meet their daily travel needs. Drawing on a systematic and inductive analysis of semi-structured interviews with older adult (65+ years of age) public transit users in Hamilton, Canada, we argue that older adults are faced with mobility work that younger and/or more able-bodied people do not routinely encounter as they meet (or attempt to meet) their daily travel needs using public transportation. Key components of older adults' trips that involve mobility work include walking to and from the bus stop, trip planning, stepping onto/ off of the bus, finding a seat, carrying items on the bus, calling a stop, and travelling in winter conditions. This mobility work can be categorized as physical (e.g., struggling to board the bus), emotional (e.g., worrying about getting a seat), or spatiotemporal (e.g., staying home when the weather is bad). Taken together, this paper puts forward a multidimensional concept of ‘mobility work’ to aid in considering accessibility at the scale of both the individual and the built environment. Further, by highlighting mobility work, this paper demonstrates the ways in which public transport spaces can be disabling for aging bodies and outlines concrete measures public transit agencies can take to make services more accessible to older adult riders.

Analysis of prospective early childhood education teachers' proposals of nature field trips: An educational experience to bring nature close during this stage

AbstractNature field trips stand out as pedagogical experiences because of their benefits in science learning, even in early childhood education (ECE). However, previous studies have found that teachers often do not make use of their full educational potential. The present research offers a descriptive exploration of ECE prospective teachers' enactment of nature field trips, detecting the difficulties they encounter and the mistakes they make. To this end, 205 videos were analyzed qualitatively to determine the pedagogical planning of nature field trips proposed and carried out by prospective ECE teachers. The results indicated that most of the participants designed nature field trips in which they could develop conceptual content related to living things through an expository method, with pupils having a passive role. They did not use activities, materials, questions, or evaluation during the trips, and neither did they integrate the trips into their classroom programming. This study adds to an understanding of how prospective teachers perceive nature field trips. Implications are presented for teacher educators working with preservice teachers to build upon prepare them for implementing nature field trips with ECE students.

Ubiquitous Computing: Driving in the Intelligent Environment

In the context of hyper-connected cars and a growing heterogeneous digital ecosystem, we wish to make the most of the data available from the various sensors, devices and services that compose the ecosystem, in order to propose a proof of concept in-vehicle system that enhances the driving experience. We focus on improving the driving experience along three main directions, namely: (1) driving and trip planning, (2) health and well-being and (3) social and online activities. We approached the in-vehicle space as a smart interface to the intelligent driving environment. The digital data-producers in the ecosystem of the connected car are sources of raw data of various categories, such as data from the outside world, gathered from sensors or online services, data from the car itself and data from the driver gathered with various mobile and wearable devices, by means of observing his state and by means of his social media and online activity. Data is later processed into three information categories—driving, wellness, and social—and used to provide multi-modal interaction, namely visual, audio and gesture. The system is implemented to act in response to the trafficked information on different levels of autonomy, either in a reactive manner, by simple monitoring, or in a proactive manner. The system is designed to provide an in-vehicle system that assists the driver with planning the travel (Drive panel), by providing a comfortable environment for the driver while monitoring him (Wellness panel), and by adaptively managing interactions with their phone and the digital environment (Social panel). Heuristic evaluation of the system is performed, with respect to guidelines formulated for automated vehicles, and a SWOT analysis of the system is also presented in the paper.

Camping, weather, and disasters: Extending the Construal Level Theory

Camping is an outdoor accommodation and type of recreation that is susceptible to weather and climate change. Camping—in addition to the relationships camping shares with weather— remains understudied despite the subsectors’ salient economic impact and high participation rate. The observable effects of non-meteorological/climatological (e.g., pandemic) is also a topic that has received limited attention. Accordingly, we introduce the Camping-Weather-Disaster (CWD) framework to examine the concurrent impact of weather and the COVID-19 disaster on post-disaster camping trip plans among leisure travelers in the 48 contiguous United States (n = 2442). Extending the Construal Level Theory, the CWD framework considers traveler construal (i.e., understanding) of a disaster and psychological distance (i.e., mental frame of reference) from a disaster alongside empirically observable state-level weather and COVID-19 cases. Results demonstrate that (1) concrete construal about timing and distance of travel is positively related to post-disaster camping trip plans; (2) weather is a significant predictor of post-disaster camping trip plans where there are regionally fewer COVID-19 cases; and (3) state-level COVID-19 cases are the most salient predictor of post-disaster camping trip plans where there are regionally more COVID-19 cases. Although the study context is camping, the CWD framework can be applied to other subsectors of tourism to build understanding and adaptive capacity to future natural conditions and disasters.

Comprehensive spatial and cost assessment of urban transport options in Munich

The constant introduction of new mobility options in urban transportation complexifies the mode choice of individual users. Relevant indicators have to give users a reliable guideline for mode choice as well as giving municipalities and industry a reasonable guideline for strategic orientation. Therefore, in this paper, a comprehensive assessment of transport options is conducted for indicators such as travel time, costs, CO2 emissions and external costs. The main added value of this paper is the method employed: Trips with origin-destination (OD) coordinates are routed for different transport modes using Open Trip Planner (OTP). In the next step, a cost model calculates these indicators for all transport modes. The presented method can compare privately-owned vehicles with different propulsion types with conventional as well as new public mobility services from a spatial perspective within the Munich area as a case study. The comprehensive information provided by these results helps mobility stakeholders in the decision-making process. The thematic analysis potential of the method is shown in figures which present the efficiency and sustainability of transport modes in different contexts. Recommendations for individual users, the mobility industry, and political decision-makers can be derived from these figures to implement measures for more sustainable and efficient mobility. Results include, that i) Individual users are more cost-efficient when using shared mobility services depending on the annual mileage (below 5,000 km for cars); ii) electric vehicles will be the most cost-efficient propulsion type and, at the same time, have the lowest CO2 emissions. The visualized interdependencies between the guiding principles for transport planners and transport modes allow providers of mobility-as-a-service (MaaS) platforms to integrate a variable pricing system according to these principles. Pricing models will be investigated in further studies.