Real Trip Research Articles

Using Electric Vehicle Driver’s Driving Mode for Trip Planning and Routing

With the increasing adoption of electric vehicles worldwide, some limitations have emerged in their usage. The main limitations include low autonomy and a scarcity of charging points. In this work, we describe a software architecture for planning a stop at charging stations along a trip, by prediction of battery charge to be spent along the path. We describe the main components of this architecture and evaluate regression methods for the car consumption prediction module. We also use a real dataset built from an electric vehicle usage to validate the architecture concept and its viability analyzing multiple linear regression machine learning models. To further validate the architecture, we make comparisons between simulated and a real trips.

The static ridesharing routing problem with flexible locations: A Norwegian case study

The municipalities in the Bergen region in Norway have recently announced a pilot project for ridesharing in the region as a means to reduce traffic congestion. As part of this project, we study the Static Ridesharing Routing Problem with Flexible Locations (SRRPFL), which aims at determining efficient routes and schedules for a set of drivers to pick up and deliver passengers at different, flexible pickup and delivery locations. We present a bi-objective mixed integer programming (MIP) model for the SRRPFL where we (lexicographically) first maximize the number of passengers serviced and then minimize the total travel times. To solve real-life instances of the SRRPFL, we propose a new Adaptive Large Neighborhood Search (ALNS) heuristic. To further improve its performance, we extend the ALNS heuristic with a local search, as well as with a set partitioning problem (denoted the Route Combination Problem) that optimally recombines the routes previously encountered in the search. The ALNS heuristic is tested on a number of test instances based on real trip data and the results demonstrate its effectiveness. The results also provide a number of insights regarding the potential benefits of ridesharing in our case study.

Віртуальний туризм - доступний вид туризму

Digitization effectively permeates every industry in this era of globalization and world change. The tourism industry is no exception, where elements of digitization and digitization are used and involved. As a result of the integration of technologies in the tourism industry, such a concept as "virtual or digital tourism" will appear. This is a modern full-fledged type of tourism, which is popular among all audiences of visitors. The phenomenon of virtualization is becoming relevant because the consumer of the service and the tourist service itself are changing, and it is becoming flexible to such visitors as inclusive tourists. But the formation of the service is influenced by other factors of world globalization - the development of technologies, psychological factors of the visitor's perception of the service. Virtual tourism, as a separate type of tourism, does not limit visitors either in movements, not in time, or in space. Therefore, virtual tourism is gaining momentum and popularity among all audiences of consumers of tourist services in the virtual world. One of its functions is as a prelude to a real trip. However, for the audience of inclusive tourists, it is a full-price and affordable replacement for traveling in physical space. But such tourists can apply for the definition category - "virtual tourist".

Threshold-guided multi-objective Generative Adversarial Networks for constructing artificial yet representative driving cycles

A Driving Cycle (DC) is a speed-time series that represents how people drive in a region. It is used to assess the vehicle’s energy and environmental performance. Constructing representative DCs has been widely studied with well-known methods. However, challenges remain regarding the reproducibility of results and the need for a large set of sampled trips, which can be costly and time-consuming. This work presents a novel approach to constructing DCs using Generative Adversarial Networks (GANs). A multi-objective training approach incorporates model-based losses represented by discriminators to capture time-series trends of speed, acceleration, and jerk, and function-based losses to ensure that the generator respects physical constraints. These losses are activated sequentially according to a convergence measure that utilizes the Characteristic Parameters (CPs) of the generated DCs for the latest activated loss. A new loss is activated if the convergence measure is lower than a threshold. An early stopping mechanism can be triggered when the threshold condition is met at the last loss. The DC generator was validated comparing the artificial trips with real trips from four cities. This comparison involved computing the Relative Difference (RD) between the CPs. The results indicate that the approach can achieve less than a 10% RD, thus providing superior results than a classical approach, a vanilla GAN, and other variations. Additionally, the threshold-based loss activation led to a reduction in training time. Transfer Learning (TL) was employed to construct DCs for other regions. This approach reduces the need for extensive data collection and ensures the reproducibility of artificial DCs.

메타버스기반 체험학습 NFT보고서의 위치인식과 ID식별 설계

In this paper, a blockchain-based platform of NFT(Non-Fungible Token) reports was proposed to ensure the reliable performance contents of field study trip. It is possible to identify and manage the personal multi biomeric authentication user ID of activity records for students in a mobile IP-based metaverse with the linkage of extended reality(XR).
 At this time, many quests and incentives of platform are provided to induce students to experience smart and fun field studying facilities by linking real trip experiences and virtual extended_reality studies in the metaverse interworking.

Polemic in the magazine "Son of the Fatherland" in 1818: on the issue of the author's problem in a documentary travelogue

The subject of the study is the ego-documents (polemical comments in the journal "Son of the Fatherland" in 1818) by V. B. Bronevsky and P. P. Svinyin – the authors of documentary travelogues about the Mediterranean campaign of Admiral D. N. Senyavin, as an example of reflection on the originality of writers' attitudes in travel notes. The purpose of the study is to determine the specifics of the author's problem in a documentary travelogue, to identify individual author's strategies in the structure of the narrative, ways of expressing the author's origin in the text. The theoretical basis of the research was the works of E.G. Mestergazi, O.V. Kublitskaya, N.A. Ermakova, A.E. Kozlov, devoted to the analysis of the narrative structure of the documentary travelogue. To understand the specifics of the ways of author's self-expression in the documentary travelogue, the following research methods are used: biographical, structural-typological, historical-literary. The scientific novelty of the research lies in the special material and the approach used: the works presented in the "thick" magazines of the XIX century rarely fall into the research field, although they often demonstrate to a greater extent both stereotypical, "fashionable" writing trends and individual authorial ones. V. B. Bronevsky insists that the subject of the narrative must absolutely coincide with the biographical author of the text, observe accuracy in the transmission of facts about the journey, describe only what the witness witnessed on a real trip. P. P. Svinyin, on the contrary, argues that the author has the right to describe not only his own impressions, but also to be guided by someone else's opinion, to convey a general impression of significant events. Using the example of the highlighted differences, it is concluded that the authors of documentary travelogues imagined the distance between the biographical author and the subject of the narrative differently, reflecting on the process of text generation.

Route Choice Set Generation on High-Resolution Networks

This study seeks to find a strategy to capture the most observed trajectories with a minimum number of algorithms. GPS information on 4,538 real trips from 131 travelers in 2008 was collected and analyzed in Minneapolis-St. Paul (the Twin Cities) as part of the I-35W Bridge Collapse study. The high-resolution road network of the Twin Cities includes 108,561 nodes and 277,747 links. Labeling and link penalty approaches are combined to generate alternatives based on either observed or free-flow speed. Overall, with the best 10 labels, on average, 40 unique routes are generated for each origin-destination pair, and around 80% of all observed trips could be captured with an 80% overlap threshold. About 88% of all observed trips have an average deviation within 50 m compared with the best matching result when combining all labels introduced in this study. Freeway-preferred routes cover more observed trips than freeway-avoided routes, and the peak coverage occurs when freeway travel is weighted between 0.8 to 1 of travel on non-freeway links. A random effects panel model is used for predicting the overlap between alternative route and observed trajectory. Multinomial and mixed logit models with a path-size term are applied to model the route selection. These models indicate that alternative routes which are shorter in distance, have faster average free-flow speed, contain a higher freeway percentage, and incur fewer traffic lights, are more likely to have higher overlap with observed trajectories and are more likely to be selected.

Synthetic Generation of Trip Data: The Case of Smart Card

While individual data are key for epidemiology, social simulation, economics, and various other fields, data owners are increasingly required to protect the personally identifiable information from data. Simple data de-identification or ‘data masking’ measures are limited, because they both reduce the utility of the dataset and are not sufficient to protect individual confidentiality. This paper provides detail on the creation of a synthetic trip data in transportation, with the Smart Card data as the case study. It discusses and compares two machine learning methods, a Generative Adversarial Network and a Bayesian Network for modelling and generating this dataset. The synthetic data retain important utility of the real dataset, e.g., the origin, destination, and time of travel, while each data point does not represent a real trip in the original dataset. The synthetic dataset can be used in various applications, including microsimulation of public transport systems, analysing travel behaviours, model predictive control of transit flows, or evaluation of transport policies.

A barrier too far: Understanding the role of intersection crossing distance on bicycle rider behavior in Chicago

For a variety of environmental, health, and social reasons, there is a pressing need to reduce the automobile dependence of American cities. Bicycles are well suited to help achieve this goal. However, perceptions of rider safety present a large hindrance toward increased bicycle adoption. These perceptions are largely influenced by the design of our current road infrastructure, including the crossing distances of large intersections. In this paper, we examine the role of intersection crossing distances in modifying rider behavior through the construction of a novel dataset integrating street widths and probable trip routes from Chicago’s Divvy bikeshare system. We compare real trips to synthetic trips that are not influenced by the width of intersections and exploit behavior differences that result from the semi-dockless nature of the bikeshare system. Our analysis reveals that bikeshare riders do avoid large intersections in limited circumstances; however, these preferences appear to be heavily outweighed by the relative spatial positions of origins and destinations (i.e., the urban morphology of Chicago). Our results suggest that specific infrastructural investments such as protected intersections could prove feasible alternatives to reduce the perception and safety concerns associated with large road barriers and enhance the attractiveness of non-motorized mobility.

Идиллия, история, рациональность: образы городов в «Действительной поездке в Германию в 1835 году» Н.И. Греча

The article explores the images of the German cities, Lubeck and Hamburg, presented in Nikolay Gretsch’s travelogue “The real trip to Germany in 1835”. The author determines the link between the images of the two cities and the tradition of describing Germany as an idyllic place. This tradition was widespread in Russian literature at the end of the 18th century — first half of the 19th century. In Gretsch’s text, Lubeck and Hamburg are depicted as idyllic but to different degrees. The locus of Lubeck is a homogeneous, patriarchal and achronous idyll, a static space that seems to have frozen in the Middle Ages. In contrast to Lübeck, the city of Hamburg is depicted as a large, contemporary, and dynamic city — in other words, as a modern type of idyll. Moreover, its orderliness goes beyond the idyll and is defined by the rational organisation of space, which is characterised by heterogeneity. Firstly, the idyllic subloci are distinguished, where the key role belongs to the demi-natural images of the garden, the park and the promenade. Secondly, the utilitarian-rational subloci of the stock exchange, quay, and canals are described. Subloci, which are marked by both idyll and rationality, have been identified (e. g. an orphanage, an almshouse). Finally, the third spatial type identified marginal sublocations of seafarers’ establishments associated with the motives of disorderli­ness — drunkenness, debauchery, etc.

Adoption of Micro-Mobility Solutions for Improving Environmental Sustainability: Comparison among Transportation Systems in Urban Contexts

Sustainable transport frameworks are gaining attention within national and international transportation policies, given the key role that decarbonisation plays in making urban environments people-friendly. Within this context, several shared services and micro-mobility options are being developed, especially as first/last mile facilities, further increasing public transport coverage levels. We present an overview of the environmental impacts of different transport modes and compare them from different perspectives, namely, CO2 emission levels, total costs (also including the user generalised cost) and service life of vehicles involved. The proposed methodology is applied to an urban context, using real trip data and showing the main findings under real conditions.

Solar photovoltaic generation for charging shared electric scooters

Scooter-sharing has been introduced as a new transportation mode. However, e-scooters have a limited battery capacity and require frequent charging, which causes the operational cost significantly high and hinders the viability of the service. To tackle this problem, this study proposes a solar charging solution with the creation of a real-time shareability network that maximizes the scooter-sharing capability and minimizes the total trip distance, constrained by e-scooters with real-time battery levels. Specifically, hourly solar potential is simulated based on a three-dimensional solar irradiation model so that photovoltaic (PV) electricity generation can be estimated when PV modules are installed at the parking stations, which enables solar charging when the origin–destination matrix of scooter-sharing trips is clustered and associated to the charging stations. As a case study in Singapore, the proposed solar charging system only needs 1–3 m2 PV modules at each station and 24%–67% of the total number of e-scooters to support almost all the real trips with a 98% reduction of trips used for charging. The system also supports 90% of on-demand mobility for at least three consecutive days without solar charging, which suggests the resilience of the system and inspires us to promote the proposed solar charging in the other global cities.

Predictive and prescriptive performance of bike-sharing demand forecasts for inventory management

Bike-sharing systems are a rapidly developing mode of transportation and provide an efficient alternative to passive, motorized personal mobility. The asymmetric nature of bike demand causes the need for rebalancing bike stations, which is typically done during nighttime. To determine the optimal starting inventory level of a station for a given day, a User Dissatisfaction Function (UDF) models user pickups and returns as non-homogeneous Poisson processes with piece-wise linear rates. In this paper, we devise a deep generative model directly applicable in the UDF by introducing a variational Poisson recurrent neural network model (VP-RNN) to forecast future pickup and return rates. We empirically evaluate our approach against both traditional and learning-based forecasting methods on real trip travel data from the city of New York, USA, and show how our model outperforms benchmarks in terms of system efficiency and demand satisfaction. By explicitly focusing on the combination of decision-making algorithms with learning-based forecasting methods, we highlight a number of shortcomings in literature. Crucially, we show how more accurate predictions do not necessarily translate into better inventory decisions. By providing insights into the interplay between forecasts, model assumptions, and decisions, we point out that forecasts and decision models should be carefully evaluated and harmonized to optimally control shared mobility systems.

G. V. Ivanov’s Essays Across Europe by Car: The Image of Germany in the 1930s

G. Ivanov’s essays Across Europe by Car describing the circumstances of the author’s real trip from Riga to Paris have signs of the genre of travel notes or travelogue. The theoretical basis the author uses to study Ivanov’s essays is the idea of the travelogue as a synthetic genre existing in a variety of forms. In the essays the article examines, travel turns into a political pamphlet. The essays meticulously reproduce the itinerary, place names, peculiarities of the way of life, architecture, and everyday details. However, the main content of the cycle focuses on Germany in the 1930s seized by the ideology of fascism. The caricature nature of the sketches borders on grotesque. Not only the leaders of Germany, but also Russian fascists (Bermondt-Avalov, Vonsyatsky, Pelhau) are depicted satirically. Describing the history of the Russian National Liberation Movement, a fascist union of Russian emigrants, Ivanov shows the reasons that attracted poverty-stricken emigrants to the organisation: the union promised protection to its members, gave them the illusion of a common cause, and seduced them with the hope of returning to Russia. The position of the author himself is quite unambiguous — he hates fascism which turns people into an obedient crowd, hypnotised by the speeches of the leaders. Sketches about Germany are framed by sketches about border towns and villages: the narrator enters through Lithuanian Šiauliai, and the description of the journey ends with his arrival at a border post in France. The beginning and the end of the essay cycle echo each other: on both sides, there are visible traces of the previous war, and the two places share a somewhat disordered but charming everyday life which is described with good humour. The main character is happy to leave Germany. However, the nation engulfed in the propaganda of its own greatness, full of belligerence already casts a shadow of threat on the neighbouring countries. At the same time, the old story of Duke Biron recounted in the essays indirectly expresses the author’s hope that every almighty despot will have to pay for his atrocities sooner or later.

Modeling the Trip Distributions of Tourists Based on Trip Chain and Entropy-Maximizing Theory

Suburban tourist railway is an emerging transportation mode for tourism. Knowing the travel demand and trip distribution patterns of tourists is an important prerequisite to the planning and construction of suburban tourist railways. However, this issue has attracted very little research attention so far. Therefore, this paper proposes a forecasting model focused on the trip distribution of tourists who travel with the suburban tourist railway. Based on the analysis of the characteristics of tourists’ trips and the use of the trip chain method, the frequency, order, distance, and visiting volume of stay points of the trips of tourists have been intensively studied. Then, a tourist trip distribution forecasting model was built in this paper. It uses the Entropy-Maximizing theory to predict trip chain distribution probability and obtain the distribution of tourists within the city. A case study that takes the H city as an example was conducted to test the proposed model. The results of this case show that the output of the model can reflect the real trip distribution characteristics of tourists very well, which demonstrates the applicability and effectiveness of the proposed model.

Modelling driver's reactive strategies in e-hailing platforms: an agent-based simulation model and an approximate analytical model

For an e-hailing taxi operation, we analyse a driver's profit-maximising reactive strategy (to either accept or refuse a ride request) in response to the ride request broadcast by the platform. We analyse four operating modes, each of which is a combination of either of two reactive strategies: no refusal and refusal based on proximity, and either of two broadcasting methods. In an operating mode, our objective is to evaluate the expected total profit in a shift. We adopt a two-stage methodology to answer the research questions. In the first stage, we develop an agent-based simulation model to capture the effect of multiple taxis on driver's reactive strategy. Using real trip data, we find that a driver could follow a strategy of refusal based on proximity and earn approximately 25% more than the baseline no refusal strategy. In the second stage, we develop an approximate analytical model for a single taxi operation and compare the performance against the agent-based simulation model. We develop closed-form expressions of the expected total profit for each operating mode and topology of the service region. We find that our approximate analytical model provides an upper bound, and the profit deviation lies within 20% of the agent-based simulation model.

On the Modelling of Emergency Ambulance Trips: The Case of the Žilina Region in Slovakia

The efficient operation of emergency medical services is critical for any society. Typically, optimisation and simulation models support decisions on emergency ambulance stations’ locations and ambulance management strategies. Essential inputs for such models are the spatiotemporal characteristics of ambulance trips. Access to data on the movements of ambulances is limited, and therefore modelling efforts often rely on assumptions (e.g., the Euclidean distance is used as a surrogate of the ambulance travel time; the closest available ambulance is dispatched to a call; or the travel time estimates, offered by application programming interfaces for ordinary vehicles, are applied to ambulances). These simplifying assumptions are often based on incomplete data or common sense without being fully supported by the evidence. Thus, data-driven research to model ambulance trips is required. We investigated a unique dataset of global positioning system-based measurements collected from seventeen emergency ambulances over three years. We enriched the data by exploring external sources and designed a rule-based procedure to extract ambulance trips for emergency cases. Trips were split into training and test sets. The training set was used to develop a series of statistical models that capture the spatiotemporal characteristics of emergency ambulance trips. The models were used to generate synthetic ambulance trips, and those were compared with the test set to decide which models are the most suitable and to evaluate degrees to which they fit the statistical properties of real-world trips. As confirmed by the low values of the Kullback–Leibler divergence (0.004–0.229) and by the Kolmogorov–Smirnov test at the significance level of 0.05, we found a very good fit between the probability distributions of spatiotemporal properties of synthetic and real trips. A reasonable modelling choice is a model where the exponential dependency on the population density is used to locate emergency cases, emergency cases are allocated to hospitals following empirical probabilities, and ambulances are routed using the fastest paths. The models we developed can be used in optimisations and simulations to improve their validity.

TRIPDECODER: Study Travel Time Attributes and Route Preferences of Metro Systems from Smart Card Data

In this article, we target at recovering the exact routes taken by commuters inside a metro system that are not captured by an Automated Fare Collection (AFC) system and hence remain unknown. We strategically propose two inference tasks to handle the recovering, one to infer the travel time of each travel link that contributes to the total duration of any trip inside a metro network and the other to infer the route preferences based on historical trip records and the travel time of each travel link inferred in the previous inference task. As these two inference tasks have interrelationship, most of existing works perform these two tasks simultaneously. However, our solution TripDecoder adopts a totally different approach. TripDecoder fully utilizes the fact that there are some trips inside a metro system with only one practical route available. It strategically decouples these two inference tasks by only taking those trip records with only one practical route as the input for the first inference task of travel time and feeding the inferred travel time to the second inference task as an additional input, which not only improves the accuracy but also effectively reduces the complexity of both inference tasks. Two case studies have been performed based on the city-scale real trip records captured by the AFC systems in Singapore and Taipei to compare the accuracy and efficiency of TripDecoder and its competitors. As expected, TripDecoder has achieved the best accuracy in both datasets, and it also demonstrates its superior efficiency and scalability.

Random Forest Model for Trip End Identification Using Cellular Phone and Points of Interest Data

Cellular phone data has been proven to be valuable in the analysis of residents’ travel patterns. Existing studies mostly identify the trip ends through rule-based or clustering algorithms. These methods largely depend on subjective experience and users’ communication behaviors. Moreover, limited by privacy policy, the accuracy of these methods is difficult to assess. In this paper, points of interest data is applied to supplement cellular phone data’s missing information generated by users’ behaviors. Specifically, a random forest model for trip end identification is proposed using multi-dimensional attributes. A field data acquisition test is designed and conducted with communication operators to implement synchronized cellular phone data and real trip information collection. The proposed identification approach is empirically evaluated with real trip information. Results show that the overall trip end detection precision and recall reach 95.2% and 88.7% with an average distance error of 269 m, and the time errors of the trip ends are less than 10 min. Compared with the rule-based approach, clustering algorithm, naive Bayes method, and support vector machine, the proposed method has better performance in accuracy and consistency.

ОПРЕДЕЛЕНИЕ ОСНОВНЫХ ТРЕБОВАНИЙ К ОТЧЕТНЫМ ФОРМАМ КОМПЛЕКСА БЕСКОНТАКТНОЙ ДИАГНОСТИКИ КОНТАКТНОЙ СЕТИ

Catenary of the electrified railways is unprotected and therefore defenseless element of a traction power supply system. Samara State Transport University has developed a complex for contactless catenary diagnostics. The complex can be installed on special diagnostic rolling stock units and on regular units of tractive and non-tractive rolling stock. The paper determines main requirements to reporting forms of the complex for contactless catenary diagnostics, provides scheme of operation, presents the results of measurements gained during real trips and shows their assessment.