Trip Planning Research Articles

Bus Travel Time Prediction Based on Ensemble Learning Methods

Buses have become indispensable in urban transportation systems, especially in developing countries. The prediction of bus travel time can provide essential information for passengers to coordinate their trip plans. By combining several prediction algorithms, ensemble learning methods have shown great potential for improving prediction accuracy in many research fields. In this article, ensemble learning methods are used to predict bus travel times. First, a novel feature-selection algorithm, Boruta, is introduced to select the appropriate input features for predicting bus travel time. The algorithm can quantify the importance of each feature and identify those that are important for the prediction. Second, we illustrate the ensemble learning methods in detail, including the bagging, boosting, and stacking methods. The representative algorithm of each category of methods is presented and utilized to study the prediction problem. Finally, a case study is conducted based on real-world data. Twenty original features are analyzed using the Boruta algorithm, and two are filtered out. Besides the ensemble learning algorithms, we also choose some other classical algorithms to predict the bus travel time. The results show that the boosting and stacking algorithms outperform other algorithms in terms of prediction accuracies.

Modelling and solving an integrated freight train scheduling and trip planning problem with hazardous materials

ABSTRACT This paper investigates the routing and scheduling of rail freight of hazardous materials with train scheduling decisions considering risk-spreading function. We consider the problem of minimizing the weighted sum of the cost of serving the commodities plus risk-spreading function using well-defined measures, either the maximum population exposure, the difference between the maximum and minimum risk, or the mean absolute deviation of the risk while limiting the population exposure below the given risk thresholds. Non-linear-mixed integer programming models and heuristic method are proposed for preparing the shipment plans and determining the schedule of train services. Numerical examples are provided to study and analyse risk-spreading functions.

Optimal driving based trip planning of electric vehicles using evolutionary algorithms: A driving assistance system

The existing driving assistance systems (DAS) are not capable to manage the electric vehicle (EV) problems namely insufficiency of charging stations and inadequate range. A novel DAS is presented here to extend the range and overcome other EV drawbacks by suggesting the driver an optimal driving strategy (ODS) continuously throughout trip performing. ODS is decided by solving a multi-objective optimization problem (MOOP), subsequently adopting a multi-criterion decision making technique. Implementation of the DAS in real application requires both better optimization results and low computational time. A study was carried out to investigate the DAS performance with four contending evolutionary algorithms (EAs), NSGAII (a non-dominated sorting multi-objective genetic algorithm), PESA (Pareto envelope-based selection algorithm), PAES (Pareto archived evolution strategy), and SPEA 2 (Strength Pareto evolutionary algorithm). After an initial investigation of EA performances based on different matrices, NSGAII and PESA were found to be most suitable. The natures of decision variables in the Pareto-optimal solutions were analyzed. After an extensive analysis based on different micro-trip structures, it was found that without considering the computational time, PESA solutions possess better convergence and diversity properties than NSGAII solutions. Various approaches were adopted to minimize DAS computation time considering both NSGAII and PESA without significantly compromising the solution’s optimality.

DYNAMIC TRIP PLANNER FOR PUBLIC TRANSPORT USING GENETIC ALGORITHM

This paper reports the development of a public transport trip planner to help the urban traveller in planning and preparing for his commute using public transportation in the city. A Genetic Algorithm (GA) approach that handles real-time Global Positioning Systems (GPS) data from buses of the Metropolitan Transport Corporation (MTC) in Chennai City (India) has been used to develop the planner. The GA has been shown to provide good solutions within the problem’s computation time constraints. The developed trip planner has been implemented for static network data first and subsequently extended to use real-time data. The “walk mode” and Chennai Mass Rapid Transit System (MRTS) have also been included in the geospatial database to extend the route-planner’s capabilities. The algorithm has subsequently been segmented to speed up the prediction process. In addition, a temporal cache has also been introduced during implementation, to handle multiple queries generated simultaneously. The results showed that there is promise for scalability and citywide implementation for the proposed real-time route-planner. The uncertainty and poor service quality perceived with public transport bus services in India could potentially be mitigated by further developments in the route-planner introduced in this paper.

Column generation-based stochastic school bell time and bus scheduling optimization

Given a trip plan (a sequence of bus stops), the integrated school bell time and bus scheduling problem simultaneously optimizes the school bell time and bus routes (the assignment of buses to serve trips at a specific time) while maintaining the minimum level-of-service requirements. The objective is to minimize the total number of buses and the total vehicle time. The previous efforts focused on the deterministic problem. However, uncertainty is unavoidable in a real-world problem. Thus, we consider this integrated problem with stochastic travel times. Two Mixed Integer Programming (MIP) models and a Column Generation-based algorithm are proposed. The algorithm incorporates the merits of the Column Generation, Simulated Annealing, Insertion Algorithm, and Greedy Randomized Adaptive Search Procedure and gains the computational power that the existing methods do not have. The experiments on a real-world problem show that, after the bell time optimization, up to 41% of current buses can be saved with even better service in terms of the higher punctuality and shorter student ride time.

Collaborative Platforms and Data Pools for Smart Urban Societies and Mobility as a Service (MaaS) from a Competition Law Perspective

In the Smart Urban society all devices and service providers will monitor, collect data and exchange data, whilst the device producers and service providers will store, distribute, analyse and re-use data on a grand scale. If firms would like to combine data, they need to give each other access either by sharing, trading, or pooling the data. On the one hand, industry-wide pooling of data could increase efficiency of certain services, and contribute to the innovation of other services, e.g., think about self-driven cars or even creating whole networks for transport available on one platform, i.e. Mobility as a Service (MaaS). On the other hand, firms combining business data may use the data, not to advance their services or products, but to collude, to exclude competitors or to abuse their market position. Indeed, by combining their data and collaborate they can gain market power, and, hence, the ability to violate competition law. We also see platforms hoarding data and designing the data architecture so to become system leaders in vertical value chains, exclusively obtain all data from various sources creating a silo or ecosystem. This paper will discuss a new platform configuration being developed regarding transport services. The new idea for a transport platform is being developed and discussed under the notion of MaaS. MaaS is a new transport paradigm that integrates existing and new mobility services, public and private transport services such as subway and e-scooters, into one single digital platform, providing customised door-to-door transport and offering personalised trip planning and payment options. The development of integrated multimodal information systems and integrated payment solutions have enabled the unfolding of the MaaS concept. We will analyse the MaaS concept from a competition law perspective, and whether the concept might be in violation of EU Competition Law, specifically Articles 101 and 102 TFEU, respectively.

Two-phase optimization model for ride-sharing with transfers in short-notice evacuations

Optimization of on-demand transportation provisions and ride-sharing services in evacuations may provide increased network capacity and enhanced evacuation performance to transportation systems and improve equity and disaster preparedness for community and society. This paper proposes a two-phase model for optimizing trip planning and operations by integrating a ride-sharing process in short-notice evacuations, to allow a joint optimization of driver-rider matching and necessary transfer connections among shared vehicle trips. In the first phase, following network topology information and personal requests, a vehicle-space-time hyper dimensional network is developed by constructing vehicle-space-time vertexes and arcs. In the second phase, based on the constructed vehicle-space-time network, a new time-discretized multi-rider multi-driver network flow model is built to formulate ride-sharing with connecting transfers. A Lagrangian relaxation solution approach is designed to solve the model in a real-world network scenario. Numerical analyses are conducted with considerations given to the three operating parameters (detour tolerance of driver, penalty factor for transfer time, and maximum allowable parking time) in the method, and the analysis results show that the proposed model can not only meet the evacuation trip needs of the participating parties but it also supports personalized requests and on-demand accesses. A small sample network is used to theoretically test the whole model and the underlying concepts and solution strategy to show each step implemented in details, and finally the applicability of the method is demonstrated using the Chicago City network.

Survey on Intelligent Truck Parking: Issues and Approaches

The truck industry transports most of the shipments in the USA. Nevertheless, the available parking infrastructure is considerably below the industry needs. The combination of parking shortage and scarcity of timely availability information hinders accurate trip planning, leading to unexpected situations and pushing drivers to park in illegal and potentially dangerous locations or to drive beyond the legal time limit. While there have been considerable efforts and technology developments in smart parking for passenger vehicles, truck parking technologies are lagging behind when it comes to development and implementation. This paper gives an overview of the current state of research on intelligent truck parking, including related studies on urban passenger vehicle parking. The surveyed work includes sensing infrastructure, resource allocation, demand and prediction models, and routing and scheduling. In addition, real data was used to compare simple historical average methods with some predictive models and illustrate the latter’s advantage.

Online popularity of destinations in Australia: An application of Polya Urn process to search engine data

The online search engine is a key source of tourism information for trip planning. In the internet, experience and feedback shared by previous tourists can be obtained with increasing ease, resulting in a feedback mechanism of interpersonal influence featured in the online peer-to-peer tourism information exchange. Such interpersonal influence may lead to a unique popularity distribution among destinations. The information exchange process is also affected by the destination choice set, which is determined by the geography of the destination countries. This study mathematically characterised the e-word-of-mouth effect in travel and tourism on the pattern formation in online destination. To achieve this, the Polya Urn process is invoked to model the clustering of destination preference and the distribution of destination popularity in an online environment. Google search volume on various destinations in Australia and France from five tourism inbound countries are used. Results show that the theoretical distribution of the Polya Urn process fits the distribution of search volume on Australian destinations, highlighting the geographical uniqueness of Australia as a closed system that possess a unique online popularity growth process and distribution.

New technologies for tourist services market development

This article analyzes the introduction of new technologies in the tourist services market. It has been determined that at present tourist enterprises are actively investing money in the process of introducing machine learning and artificial intelligence technologies; actively use automation of various levels of complexity of business processes. It is concluded that the sales technologies of the tourism product and individual tourism services are based today on the intensive implementation of modern technological solutions. It is noted that the main trends in the development of the tourist services market will be the further development of online tourism in Russia, as well as the widespread use of Internet technologies.Further penetration of technologies that simplify the routine operations of tourists is predicted. This, for example, real-time luggage tracking via phone, combining trip planning and booking services in one application. At the same time, it will be implemented in the B2B segment. New tourist platforms are being developed today, in which all content from a wide variety of sources is presented, whether it is the traditional GDS, NDC booking system, airline’s own APIs or aggregator data.

A Hybrid Deep Learning-Based Traffic Forecasting Approach Integrating Adjacency Filtering and Frequency Decomposition

Traffic forecasting in urban area has attracted substantial attention in recent years due to its significant assitance for traffic dispatching and trip planning. However, this task is very challenging due to the complex dependencies inherent in the traffic process. In our study, recent and periodic dependencies are identified and used to describe the corresponding near-term and long-term effects of historical traffic data on future traffic states. Subsequently, during the recent dependency modeling, each road is found to correlate with its adjacent roads through traffic flow diffusion, then the correlation intensities are quantified and used to choose strongly correlated roads to build a critical road sequence. While for the periodic dependency modeling, since the historical speed series of a road segment exhibits multi-frequency attributes (i.e., low-frequency daily period and high-frequency stochastic fluctuation), wavelet transform is conducted to decompose the original speed series into low and high-frequency sub-series. On these bases, we propose a hybrid traffic speed forecasting model, flow and wavelet-integrated spatio-temporal network (FW-STN). In the FW-STN, the recent features are captured by the convolutional neural network (CNN) with near-term traffic data from the derived critical road sequence, and the periodic features are captured by the long short-term memory (LSTM) with the low and high-frequency sub-series. Both the recent and periodic features are then fused to conduct the final prediction. Experimental results on real traffic data show that the proposed approach outperforms nine state-of-the-art methods (with improvements of 3% ~15% in mean average percentage error).

Mobility as a Service (MaaS): The Importance of Transportation Psychology

Mobility-as-a-Service (MaaS) is based on the notion that consumers and transport providers access a centralized platform for the planning, payment, and management of trips, and combines multiple modes of transportation designed to increase the efficiency of the system. MaaS offers substantial societal benefits, including the reduction of emissions, traffic congestion, road injuries, and the overall discomfort associated with travel, in addition to providing personalized transportation solutions. Since the delivery of these benefits hinges on the widespread adoption of MaaS platforms, we draw on consumer psychology for insight into the social and cognitive psychological factors that may hamper the adoption of MaaS, and their influence on consumer choices and perceptions. More generally, this paper highlights that transportation is a fertile context for consumer psychology research.

Elasticity of Tourism Demand in the Modern World

The article is devoted to the analysis of regularities of tourism demand formation in the modern world. It has been established that the high degree of demand differentiation provides tourists with the opportunity to choose products that are diverse in terms of quality, range of additional services and travel destinations. The authors have proved that tourists strive to independently select individual components, form tours and determine tour programs. It has been revealed that the remoteness of tourism demand in time and place from tourism supply necessitates premature planning of most tourist trips since the demand for most tourism products is seasonal. The authors have proposed to regulate the seasonality of demand for tourism services by various marketing measures, such as price differentiation and discounts, development of off-season types of recreation aimed at transferring part of the demand from the intensive season to another period, as well as reduction of the cost of tourism services.

Family memory trips – children's and parents’ planning of adoption return trips

ABSTRACT It can be argued that adoption return trips are a new travel industry. Today, transnational adoptive families make adoption return trips while their children are very young. The aim of such trips is to create positive links between the child and their birth country. Based on interviews with ten Swedish transnational adoptive families, this qualitative and interdisciplinary study sets out to explore how adopted children and their parents plan return trips. The analysis shows that both children and parents mobilise memories of places, people and heritage sites from the birth countries while planning their trips. In theoretical terms, the study draws on family tourism in combination with child studies and ‘personal memory tourism.’ The article challenges the idea that adoption return trips are exclusively for the benefit of the adoptee and suggests that the family planning of adoption return trips makes them into ‘family memory trips.’

Prototype of REST Location-based Service for Trip Planning by Paratransit (Mikrolet) in Malang City

Paratransit (mikrolet) was very popular in Malang city. However, their popularity is reduced by the availability of wide variety of more modern and online public transportations. One of the obstacles commonly faced by passengers of mikrolet is the limited information about the routes taken by mikrolet. In this paper, we proposed a prototype of service that can generate trip planning by mikrolet. We use depth-first search (DFS) algorithm as the basis to build our solution because it is relatively simple to modify. The service for trip planning available through REST web service and utilize location-based service style. Based on functionality testing result, the proposed system successfully generate all trip plans as expected. From performance perspective, when the number of routes and plans increased, the overall system performance degrades, so in the future the proposed prototype and its algorithm may needs further optimization.

A two-phase optimization model for the demand-responsive customized bus network design

This paper proposes a new optimization model for the network design problem of the demand-responsive customized bus (CB). The proposed model consists of two phases: inserting passenger requests dynamically in an interactive manner (dynamic phase) and optimizing the service network statically based on the overall demand (static phase). In the dynamic phase, we propose a hierarchical decision-making model to describe the interactive manner between operator and passengers. The CB network design problem is formulated in a mixed-integer program with the objective of maximizing operator’s revenue. The CB passenger’s travel behavior is measured by a discrete choice model given the trip plan provided by the operator. A dynamic insertion method is developed to address the proposed model in the dynamic phase. For the network design problem in the static phase, the service network is re-optimized based on the confirmed passengers with strict time deviation constraints embedded in the static multi-vehicle pickup and delivery problem. An exact solution method is developed based on the branch-and-bound (B&B) algorithm. Numerical examples are conducted to verify the proposed models and solution algorithms.

Group trip planning and information seeking behaviours by mobile social media users: A study of tourists in Australia, Bangladesh and China

Social media plays an increasingly important role in travel information seeking and decision-making. However, there is limited understanding of how a group of tourists use social media to plan trips collaboratively and the different practices between countries. In this study, we investigated the collaborative information seeking (CIS) and sharing behaviours of mobile social media users from Australia, Bangladesh and China. Specifically, we surveyed a total of 219 participants to explore the differences in CIS behaviours when people were planning a group trip. The findings suggest significant differences among three countries in terms of the motivations of using social media, CIS activities and social interactions outside the group. Key findings include Bangladeshi and Chinese travellers preferred known contacts on social media, while Australian tourists intended to use both known contacts and user-generated contents for seeking information. The findings also show that social interactions employed by individuals are considered as an important complement of and are interwoven with in-group CIS; both contribute to tourism information seeking. Finally, we propose a framework for CIS research in the tourism domain.

Efficiency Test of K-Means Clustering Algorithm on Collaborative Trip Advisory and Planning System

Efficiency Test of K-Means Clustering Algorithm on Collaborative Trip Advisory and Planning System

Smartphone and daily travel: How the use of smartphone applications affect travel decisions

This paper aims to explore the covariates that affect the use of smartphones for trip planning as well as the covariates of perceived impact of smartphone use on travel outcomes. Trip planning activities considered in the study include performing online tasks, deciding departure time, mode choice decision, deciding trip destination and communicating and coordinating trips by using smartphones. Travel outcomes include the number of new places visited, social gatherings attended and trips planned in groups. This study emphasizes the role of built-environment and attitudinal variables on smartphone use for travel. Following ordered response and binary choice modeling approach, this study identifies that age and attitudes (toward smartphone use and environment) play a significant role in using smartphones for trip planning as well as shaping travel outcomes. Millennials (16–34 years) are more likely to use smartphones for trip planning and perceive increase in travel outcomes compared to other age groups. In addition, tech-savvy attitude is positively associated with trip planning and travel outcomes. However, the influence of built-environment characteristics is comparatively low in relation to the other attributes considered in the analysis. The study also finds that those who highly use smartphone apps for social networking purposes are more likely to perceive increase in their travel outcomes.

The automated and unmanned inland vessel

Automation is an ongoing development that has already changed the inland waterway transport sector (IWT) fundamentally. One of the next possible steps is the technology for fully automated operation systems under conditions and with the possibility for human intervention from on-shore. This article develops and explores a fully automated and unmanned inland vessel and questions if a positive business case for the IWT and society is possible. It also identifies the success and failure factors of this innovation. Two analyses were applied regarding barriers that could prevent market uptake (System of innovation approach, SIA), and the social costs and benefits of automation (Social Cost/Benefit Analysis, SCBA). The innovation has the potential to be disruptive in the entire supply chain as all transport modes are discovering their automation potential. The technology is assumed to have a possible impact on vessel safety, trip planning, fuel efficiency and even freight capacity. Also the institutional framework of the European IWT is taken in account regarding crew requirements and safety standards.