Trip Planning Research Articles

A fundamental diagram based hybrid framework for traffic flow estimation and prediction by combining a Markovian model with deep learning

Accurate traffic congestion estimation and prediction are critical building blocks for smart trip planning and rerouting decisions in transportation systems. Over the decades, there have been many studies focusing on traffic congestion estimation and prediction with different statistical approaches (e.g., Markov chain) and machine learning models (e.g., clustering, Bayesian networks, and artificial neural networks). However, there is a lack of a unified framework to address the mechanisms of different models and integrate the advantages of different methods through combinations. This paper introduces the FD-Markov-LSTM model, a hybrid interpretable approach that combines the fundamental diagram (FD), Markov chain, and long short-term memory (LSTM). The aim is to estimate and predict traffic states by integrating statistical data in both congested and uncongested scenarios. The FD-Markov-LSTM model leverages the FD to identify hierarchical traffic states and utilizes the Markov process to capture the probabilistic transitions between these states. We employ the LSTM model to further capture the residual time series produced by the Markov chain model (assuming a memoryless property) to enhance the estimation and prediction performance. The proposed model's accuracy in estimating and predicting traffic flow is evaluated using empirical data from three case studies conducted in Beijing and Los Angeles. The results highlight a significant improvement in accuracy compared to classical benchmark models such as the Markov model, ARIMA model, k-Nearest Neighbor model, Random Forest model, and LSTM. Specifically, the FD-Markov-LSTM model achieves reductions of over 39% in mean absolute error, 35% in root mean squared error, and 7.4% in mean absolute percentage error. These results clearly demonstrate that the FD-Markov-LSTM model outperforms the benchmark models, enabling more precise predictions of traffic flow.

Modeling virus transmission risks in commuting with emerging mobility services: A case study of COVID-19

Commuting is an important part of daily life. With the gradual recovery from COVID-19 and more people returning to work from the office, the transmission of COVID-19 during commuting becomes a concern. Recent emerging mobility services (such as ride-hailing and bike-sharing) further deteriorate the infection risks due to shared vehicles or spaces during travel. Hence, it is important to quantify the infection risks in commuting. This paper proposes a probabilistic framework to estimate the risk of infection during an individual’s commute considering different travel modes, including public transit, ride-share, bike, and walking. The objective is to evaluate the probability of infection as well as the estimation errors (i.e., uncertainty quantification) given the origin-destination (OD), departure time, and travel mode. We first define a general trip planning function to generate trip trajectories and probabilities of choosing different paths according to the OD, departure time, and travel mode. Then, we consider two channels of infections: 1) infection by close contact and 2) infection by touching surfaces. The infection risks are calculated on a trip segment basis. Different sources of data (such as smart card data, travel surveys, and population data) are used to estimate the potential interactions between the individual and the infectious environment. A first-order approximation is used to simplify the computational complexity. We also derive the closed-form formulation for the estimation errors, enabling us to quantify the uncertainty of the estimation results. The model is implemented in the MIT community as a case study. We evaluate the commute infection risks for employees and students. Results show that most of the individuals have an infection probability close to zero. The maximum infection probability is around 1.3%, implying that the probability of getting infected during the commuting process is low. Individuals with larger travel distances, traveling in transit, and traveling during peak hours are more likely to get infected. Practical implementations of the model are also discussed.

Why do social media users follow tourism-related posts? Roles of bloggers and posts in trip planning

PurposeMore people share their tourism experiences on social media today than in the past, and as a result, more people follow these posts in their trip planning. However, studies into tourists' intention to follow such posts are scarce. Therefore, this study investigates the antecedents influencing social media users' intentions to follow tourism-related posts (TRPs) when planning their trips.Design/methodology/approachQuestionnaires were collected from 402 social media users who had followed TRPs for their trip planning. Data were then analyzed using partial least squares structural equation modeling (PLS-SEM) and artificial neural networks.FindingsThe authors found that blogger–user fit and users' involvement with TRPs influenced behavior components (attitudes toward TRPs and intention to follow TRPs) via assessment components (bloggers' credibility and content quality), and the authors developed a framework to explain this relationship.Originality/valueThe findings advance prior studies by investigating (1) the antecedents of intention to follow TRPs when trip planning, (2) the two main social media elements – bloggers and posts – to understanding the role of social media on travel behavior and (3) involvement with TRPs and their impacts on travel behavior. This study contributes to the research on social media and tourism marketing and proposes practical indications for bloggers, social media platforms and destination marketing organizations.

Traffic speed prediction using GARCH‐GRU hybrid model

AbstractTraffic speed prediction is an integral part of an intelligent transportation system (ITS) because an advanced knowledge of traffic speed can help taking proactive preventive steps to avoid impending problems and it can also help in trip planning. Traffic speed data comprises a time series that may be modelled using any statistical or machine learning technique. In most of the cases, however, the performance of such models is bottlenecked due to heteroskedasticity usually present in such datasets. ARCH/GARCH family of models are generally used to model variance in such data. This paper presents a novel technique, termed as GARCH‐GRU, based on additive decomposition that splits data into random (residual) and deterministic parts. Random part is normalized using rolling standard deviation. GARCH (1, 1) is used to predict conditional variance of the residual and the predicted variance is then used in the basic model equation along with normalized residual that mimic white noise as required by the model. The data other than residual is modelled using a GRU model. The approach is applied to two datasets corresponding to a downtown road and a motorway. For comparison, the same datasets are exposed to three classical techniques; seasonal ARIMA, CNN and GRU techniques. The results demonstrate that the GARCH‐GRU technique outperforms others for random data of downtown road but fails to handle dynamic variations present in the motorway data.

Interprofessional Field Experiences in Occupational Safety and Health.

Field trips are beneficial to students, because they provide experiences outside of the traditional classroom. Incorporating field trips into graduate programs can increase students' exposures to real world experiences so that they can incorporate that knowledge as they complete their program. The purpose of the project was to collect and analyze graduate student feedback on 13 in-person interprofessional field trips focused on occupational safety and health. Data were collected through post-field trip structured discussions. Content analysis was used to determine themes. Five themes emerged from the data: Personal Value, Networking and Meeting, Health and Safety Planning and Policy, Environment, and Logistics and Planning. Field trips are valuable learning experiences for graduate students. The field trips in this study offered concrete experiences in occupational safety and health. Post-field trip, students provided feedback through structured discussions, which allowed for reflective observation. Overall, students found personal value in the field trips, observed health and safety procedures and policies in action, learned about various work environments, and provided input on the logistics and planning of field trips.

A multi-modal tourist trip planner integrating road and pedestrian networks

The Tourist Trip Design Problem aims to prescribe a sightseeing plan that maximizes tourist satisfaction while taking into account a multitude of parameters and constraints, such as the distances among points of interest, the expected duration of each visit, the opening hours of each attraction, the time available daily. In this article we deal with a variant of the problem in which the mobility environment consists of a pedestrian network and a road network. Hence, a plan includes a car tour with a number of stops from which pedestrian subtours to attractions (each with its own time windows) depart. We study the problem and develop a method to evaluate the feasibility of solutions in constant time, to speed up the search. The proposed method is embedded into an ad-hoc iterated local search. Experimental results show that our approach can handle realistic instances with up to 3643 points of interest (over a seven day planning horizon) in few seconds.

Sustainable group tourist trip planning: An adaptive large neighborhood search algorithm

The tourism industry is a key driver of economic growth and contributes to the achievement of sustainability goals. This paper presents a multi-objective group tourist planning problem that considers economic, environmental, and social dimensions simultaneously. The proposed model minimizes total cost and environmental impacts while maximizing the total collected prizes from tourists' interests. We introduce the lost profit opportunity for the cost of tours from an economic perspective for the first time. From an environmental perspective, the model minimizes both carbon emissions for transportation and the waste produced by tourists. Social satisfaction is addressed by considering tourists' preferences for visiting tourist sites and their interests in participating in group tours, maximizing total collected prizes. Uncertainties in travel time and prize values are addressed by using a fuzzy programming approach. A multi-objective adaptive large neighborhood search (ALNS) algorithm is developed to solve the proposed multi-objective group tourist planning problem, offering various removal, insertion, and local search heuristic procedures. Extensive analyses and computations are conducted to demonstrate the performance of the proposed multi-objective optimization model and the ALNS metaheuristic algorithm in solving large-scale instances. The results demonstrate the effectiveness of our approach in aiding tourism managers to make informed decisions that balance economic, environmental, and social objectives.

Another Tragedy in Outdoor Education

The legalities around incidents in outdoor education (OE) are undeniable, and ignorance is not a defence. Using case study analysis specific to legal literacy, we extend this analysis to include the “7 Rights” framework and add two additional rights for consideration, referred to as the 9 Rights. This discussion organizes what is publicly available on the Toronto District School Board (TDSB) outing, and death of Jeremiah Perry, and draw insights from the decision of the Ontario Superior Court of Justice in the subsequent criminal negligence trial. We present our analysis and recommendations, based on the 9 Rights. By exploring pertinent legal terminology, relevant to this case, the 9 Rights guides risk analysis for trip planning, in-field risk assessment, and risk management. The unfortunate crux of this discussion, and many OE incidents in Canada, is that the law enters the learning equation only after something goes wrong.

Safe Optimal Vessel Planning on Natural Inland Waterways

Despite inland ports play an essential role in today’s global logistics chain, many problems still remain unsolved. One of the main challenges that inland waterways face is the trip planning of cargo vessels. A correct trip planning does not only improve the efficiency and prestige of the port, but is also crucial to ensure the safety of operations. This issue is particularly critical in the case of natural waterways whose depth and width are conditioned by natural phenomena that, in principle, cannot be controlled. This is the case of the Guadalquivir river, a waterway that connects the Atlantic Ocean and the inland Port of Seville in the south of Spain. In this context, this work proposes a two-step solution to optimize the time needed for vessels to complete their journey through the waterway while considering the constraint imposed by the time-varying depth and encountering situations. The outcome is the set of times when vessels are required to cross a series of boundaries delimiting the different sections of the waterway. The advantages of the proposed approach are studied in contrast to a first-arrived first-served scheduling solution in terms of optimality and feasibility.

The effect of data science on urban sustainability through the optimization of demand-responsive transportation

The study investigated the viability of implementing a demand-responsive transportation (DRT) system within the premises of Al-Baha University to facilitate the punctual arrival of students to their classes and ensure their convenient departure from the campus. This study aims to demonstrate and elucidate how data mining enhances the efficiency of DRT systems. The case study investigates the potential improvements in DRT operations through the utilization of data mining techniques. The research employs descriptive and prescriptive techniques for mining trip planning data. The database maintained by the Deanship of Admission and Registration et al.-Baha University in Al-Baha City, Saudi Arabia, has identified 50 designated locations for student pick-up. The research establishes a comprehensive database that classifies pick-up points as spatial vectors, incorporating longitude, latitude, and counts of students. Cluster analysis and multidimensional scaling techniques are employed to minimize travel times and enhance operational efficiency by consolidating pick-up points. A completely adaptable DRT system prioritizes user satisfaction over operational efficiency, facilitating superior flexible services with fewer buses. The University Transport Department can implement Fixed Route systems in regions with significant population density and substantial demand. Conversely, in areas with a lower population, where Fixed Route systems may be less efficient, the department can opt for DRT systems. Additionally, the findings demonstrate the potential cost-saving benefits of implementing a hybrid system that integrates the university and off-campus student housing. The results indicate that providing fully flexible DRT services to students’ harms efficiency and user experience. A comparative analysis between the Fixed route system and the DRT system reveals that the latter has the potential to offer enhanced service to students, utilizing an equivalent number of vehicles while reducing the number of rides required, all while maintaining a higher degree of flexibility in service provision. According to the recommendation of this study, it is advisable to implement a hybrid fixed route and DRT system. However, to assess the feasibility of such a system in a university setting in the Kingdom of Saudi Arabia (KSA), it is necessary to conduct a comprehensive viability analysis to identify any potential challenges or concerns.

Social media sites and nightlife tourism: an empirical study of tourist decision-making on nightlife entertainment consumption

PurposeThis paper aims to investigate the effect of tourists’ social media usage when planning trips on the probability of tourists consuming nightlife activities and their spending on nightlife consumption.Design/methodology/approachTwo thousand questionnaires comprising questions about social media usage, consumption of nightlife entertainment and decisions to consume nightlife activities are distributed to international visitors at the departure hall of Phuket International Airport. A series of regressions are estimated. A Heckman two-step correction model is conducted to avoid the potential of selection bias.FindingsResults show that the information available on Facebook positively links with the probability of tourists consuming nightlife activities. Furthermore, findings indicate that the usage of YouTube in tourist trip planning could statistically influence tourists to spend more on their nightlife consumption. Therefore, the results suggest that entrepreneurs could use Facebook to stimulate tourists’ nightlife consumption, while YouTube could be used to elevate tourists’ spending on nightlife consumption. The influence of these two social media sites on tourists’ decisions aids the authorities in the mitigation of the negative impacts of nightlife tourism.Originality/valueGiven the increasing role of social media in tourists’ consumption choices, to the best of the authors’ knowledge, this is the first paper to quantitatively explore the relationship between tourists’ social media usage and nightlife consumption in Thailand. The revealed relationship between the use of specific social media sites (Facebook and YouTube) and tourists’ decisions to consume and the amount spent on nightlife consumption could be used by nightlife tourism entrepreneurs and destination marketers in their marketing campaigns, yielding competitive advantages. Meanwhile, related authorities and non-profit organisations could apply this study’s findings to demarket tourists’ consumption of nightlife activities in areas affected by the negative impacts of tourists’ nightlife consumption.

Estimating Toll Road Travel Times Using Segment-Based Data Imputation

Efficient and sustainable transportation is crucial for addressing the environmental and social challenges associated with urban mobility. Accurate estimation of travel time plays a pivotal role in traffic management and trip planning. This study focused on leveraging machine learning models to enhance travel time estimation accuracy on toll roads under diverse traffic conditions. Two models were developed for travel time estimation under a variety of traffic conditions on the Don Muang Tollway, Bangkok, Thailand: a long short-term memory (LSTM) recurrent neural network model and a support vector regression (SVR) model. Missing data were treated using the proposed segment-based data imputation method. Unlike other studies, the effects of missing input data on the travel time model performance were also analyzed. Traffic parameters, such as speed and flow, along with other relevant parameters (time of day, day of the week, holiday indicators, and a missing data indicator), were fed into each model to estimate travel time on each of the four specific routes. The LSTM and SVR results had similar performance levels based on evaluating the all-day pooled data. However, the mean absolute percentage errors were lower for LSTM during peak periods, while SVR performed slightly better during off-peak periods. Additionally, LSTM coped substantially better than SVR with unusual traffic fluctuations. The sensitivity analysis of the missing input data in this study also revealed that the LSTM model was more robust to the high degree of missing data than the SVR model.

Assessment of drivers and barriers in the adoption of Mobility as a Service (MaaS): a case study of Noida, India

ABSTRACT Mobility as a Service (MaaS) has emerged as a transformative concept in urban transportation, integrating multiple modes of transportation through digital platforms to provide seamless travel experiences. By utilizing smartphone applications, MaaS simplifies trip planning, real-time information access, and consolidated payment systems, offering convenience to users. However, MaaS research has primarily focused on developed countries with well-established transport systems, making it crucial to explore its potential and challenges in developing cities of the global south, such as Noida in India. In this study, Noida, a satellite town of NCT Delhi, was chosen as only case study to gather data on user behaviour and preferences regarding app-based mobility services. A comprehensive survey collected information on socio-economic factors, personal vehicle ownership, commuting patterns, public transport usage, and attitudes towards digital ecosystems and app-based mobility services. Principal Component Analysis and K-means Clustering techniques were applied to identify distinct user types and categories, providing insights into user preferences and expectations. The analysis of the collected data revealed user clusters and their respective characteristics. The sustainability aspects of on-demand mobility services were evaluated, comparing user perceptions with private vehicle usage. The study also examined the impact of app-based mobility services on public transport and identified barriers and constraints specific to different user clusters, contributing to a better understanding of the feasibility of implementing MaaS. The findings will provide valuable insights for policymakers and transportation authorities, enabling the development of strategies and interventions to enhance urban mobility and foster MaaS adoption. By addressing the specific needs and preferences of users, MaaS can play a significant role in improving the efficiency and sustainability of transportation in complex urban environment cities like Noida in India.

An Intelligent Support System to Help Teachers Plan Field Trips

AbstractField trips enrich learning programs with out-of-school activities that can bring gains in students’ academic content knowledge and personal growth. However, they are a source of anxiety for teachers because of the bureaucracy, pedagogy, etc., risks they imply. To address this issue, we propose FieldTripOrganizer, a field trip planner based on the mixed-initiative approach aimed at increasing teachers’ autonomy and motivation in designing educational tours. The key aspects of our application are (i) the simultaneous provision of information filtering and automated scheduling support while the user designs the field trip, and (ii) the visual annotation of places and activities to show whether they can be included in the itinerary without violating its time constraints. Different from current tour planners, these functions enable the user to be in full control of the design process, delegating the system to manage difficult and burdensome tasks such as consistency checks and itinerary optimization. We evaluated FieldTripOrganizer in the use case of organizing a science field trip. In a preliminary user study involving 18 science teachers, our application turned up to be superior to a baseline tour planner in both usability and user experience. Moreover, the teachers declared that it was helpful, motivated them, and reduced their anxiety during the design of the field trips.

The visual complexity of bike maps

Abstract. More and more cities try to encourage residents to cycle more. Therefore, governments are developing comprehensive bike maps to facilitate trip planning and increase the popularity of cycling. However, research on the topic of bike maps is rare and the versatility of possible features shown on a bike map makes these visually more complex than others. To understand how base maps and the display of cycling related features affect the visual complexity of bike maps and thus their effectiveness, we used different metrics (GMLMT, Subband Entropy, Edge Density, Feature Congestion, and Distinct Object-Type Counts) on four bike maps with four different visual complexity levels. We ran an eye-tracking experiment with 35 participants solving four different everyday tasks with these four bike maps. The findings suggest that adding more detail to base maps and displaying more cycling related features on a map resulted in a visually more complex bike map. Size, shape, and colour were found to have the biggest influence on the applied metrics. The analysis of eye-tracking data revealed that the display of cycling related features can affect the time needed for successfully completing a task. To deepen the gained understanding, further research should in more detail investigate how base maps influence bike maps efficiency.

Autonomous shuttles contribution to independent mobility for children – a qualitative pilot study

Children's mobility and the opportunity to get around the city on their own has decreased and is today limited to a large extent by unsafe traffic situations. Due to their limited independent mobility, children are excluded from the urban space and its facilities. Independent mobility can be related to several dimensions of children's social, physical, mental and cognitive wellbeing. The purpose of this study is to investigate if and in what way autonomous buses can contribute to children's independent mobility and, using children's experiences, provide insights into this before the introduction of autonomous buses in cities and public transport systems. To do so, the two research questions "How can autonomous buses in the transport system contribute to children's independent mobility?" and "What prerequisites are necessary for autonomous buses to contribute to children's independent mobility?" are answered. The survey is based on a case of introduction of autonomous buses in urban environments. Three focus groups with students in a primary school were conducted with the aim of gaining the participants' experiences. The results indicate that the introduction of autonomous buses in an urban area can contribute to children's independent mobility in several ways and that the buses have certain characteristics that enable them to be adapted to children's conditions. The low speed of the shuttles, which is often seen as an obstacle when it comes to time efficiency, has a positive effect on perceived safety in this case. The mobility solution is also child-friendly in that it is accessible in terms of trip planning and payment. However, it is important to consider the risk that the bus might replace active transportation modes, like cycling. To avoid this, autonomous shuttles should be integrated in a way that complement, not compete with, active travel.

Trip planning for a mobility-as-a-service system: Integrating metros and shared autonomous vehicles

Mobility as a service (MaaS) integrates various transport modes into an on-demand and real-time platform, providing door-to-door service, and has received extensive attention. For MaaS, personalized trip planning is important but intractable. In this paper, we present a two-phase decision-support optimization framework for the problem of a MaaS system incorporating metros and shared autonomous vehicles (SAVs). First, a mixed integer programming model is proposed to optimize the routes of heterogeneous travelers considering five transport mode combinations, in which SAVs are regarded as not only a first- and last-mile connector to the metro but also a competitor. Next, the scheduling of SAVs and departure time of each traveler is determined with the purpose of minimizing the SAV operation cost. To apply the proposed framework to scenarios with real-time requests, we adopt the rolling horizon solution method, which includes four sub-modules. The method is evaluated on the Sioux Falls network, and the experimental results show that travelers become more sensitive to the mode choice as the additional time of the metro increases. In addition, the connectivity of the metro network has a considerable influence on the relationship between the metro and SAVs. The methodology can be useful for the trip planning of other transport mode combinations.

Mobility as a service and socio-territorial inequalities: A systematic literature review

Mobility as a service is a potential solution to mobility problems; however, it raises concerns about its relationship with socio-territorial inequalities (STIs). This paper contains a systematic literature review of real-world MaaS applications and their effects on STIs. From the principle of distributive justice, we adopted the Resources, Opportunities, Outcomes, and Wellbeing (ROOW) approach to assess cases. From 2009 papers on MaaS, we identified 20 that stood as real-world applications that considered equity impacts. Most studies were undertaken in Europe and Asia, neglecting countries in South America, Africa, and other low-income countries. They did not quantify the societal advantages of MaaS, while only a handful investigated the influence of MaaS over STIs. Results indicate that MaaS schemes contain at least three factors that may drive inequality: the lack of basic resources to enter the system, the systems’ limited geographic coverage, or MaaS users may simply not gain from the system’s intended accessibility benefits. In conclusion, MaaS could improve trip planning and access to new modes and low-density areas, but it is still perceived as expensive and only accessible to digitally literate people. This should be considered when defining MaaS governance, which remains (to date) underdeveloped, hindering private-public collaboration.

Online tourist information search strategies

Technological advances continue to significantly change tourist information search behaviour. This paper revisits online tourist information search strategies by reconceptualising them as a combination of goals, approaches and online sources. Based on pre-trip and post-trip interviews and trip planning diaries, the paper investigates how tourists search for information online. An online tourist information search strategy framework is developed by considering how tourists integrate approaches and sources to meet their information search goals. The key contributions of the study are the redefinition of tourist information search strategy in an online context, a classification of sources that more suitably reflects online information-scapes, and the identification of interesting and often complex search patterns that suggest changes in information search strategies over the course of trip planning.

A Study on Customer Behaviour Towards Makemytrip.com

India's tourist business has grown tremendously in recent years .The days of making many calls to agents in search of travel information are long gone. Today, one may access a wealth of knowledge from the comfort of their home, around-the-clock, thanks to the World Wide Web. With only a click of a button, you can get information on hotels, tickets, local transport, and even the sights and sounds of a certain location. Customers all across the world are increasingly using the internet to fulfil their travel demands. The ease of the internet is becoming increasingly important to customers' demands for both personal and professional travel, from trip planning and price comparison to purchasing and booking. Today's travel firms are required to continuously reinvent themselves because of the ever changing traveler. Due to the growth and influence of the internet, travel agencies must now actively promote themselves online and on mobile devices. It makes sense given that the internet travel market has expanded rapidly during the past 10 years. With reference to MakeMyTrip.com, this research paper examines and evaluates the usefulness of travel websites.