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Travelers always perform some preference during the decision-making process. The preference will affect the decision results and can be improved by continuously learning. In order to understand the influence of individual preference on travel behavior choice , two individual preferences, including indifference preference and compulsive preference are considered in the paper. Two updating mechanisms of compulsive preference are proposed to obtain the choosing probability of all alternatives. Reinforcement learning models are established integrating the gain stimulating and loss stimulating considering expected utility. Nguyen Dupuis network is adopted for numerical simulation to study the updating process. Simulation results denote that the equilibrium state is much more efficient when preference learning mechanism is considered comparing with the traditional stochastic user equilibrium model, and can decrease the total travel time greatly, which can be applied for urban traffic management. Personalized traffic guidance is the effective solution to traffic congestion in the future [ABSTRACT FROM AUTHOR]

The emergence of electric vehicles offers a promising approach to achieving a more sustainable transportation system, given their lower production of direct emissions. However, the limited driving range and insufficient public recharging infrastructure in some areas hinder their competitiveness against traditional vehicles with internal combustion engines. To address these issues, this paper introduces an "iterative route cover optimization method" to suggest charging station locations in high-demand regions. The method samples routes from a route choice set and optimally locates at least one charging station along each route. Through iterative resampling and optimal allocation of charging stations, the method identifies the potential recharging demand in a location or a region. We demonstrate the method's applicability to a transportation network of the southern part of Sweden. The results show that the proposed method is capable to suggest locations and geographical regions where the recharging demand is potentially high. [ABSTRACT FROM AUTHOR]

This paper evaluates how the route overlapping feature affects the stochastic assignment paradox by comparing the multinomial logit (MNL) model and two path-size logit (PSL) models. Two circumstances, (1) improving an existing link and (2) adding/removing a new link, are studied. The paradoxical features of three models are illustrated and compared mathematically and graphically in four networks with route overlapping links: (1) a single origin-destination (O-D) pair network with four links; (2) a single O-D pair network with five links; (3) a multiple O-D pair network with seven links; and (4) an application in a road sub-network of Harbin City. The general conclusions are significant for traffic planners and engineers when those kinds of route choice models are used to evaluate traffic planning, management, and control policies/measures. [ABSTRACT FROM AUTHOR]

Technological advancements are rapidly changing traffic management in cities. Navigation applications, in particular, have impacted cities in many ways by rerouting traffic. As different routing strategies distribute traffic differently, understanding these disparities across multiple city-relevant dimensions is extremely important for decision-makers. We develop a multi-themed framework called Socially- Aware Evaluation Framework for Transportation (SAEF), which assists in understanding how traffic routing and the resultant dynamics affect cities. The framework is presented for four Bay Area cities, for which we compare three routing strategies - user equilibrium travel time, system optimal travel time, and system optimal fuel. The results demonstrate that many neighborhood impacts, such as traffic load on residential streets and around minority schools, degraded with the system-optimal travel time and fuel routing in comparison to the user-equilibrium travel time routing. The findings also show that all routing strategies subject the city's disadvantaged neighborhoods to disproportionate traffic exposure. Our intent with this work is to provide an evaluation framework that enables reflection on the consequences of traffic routing and management strategies, allowing city planners to recognize the trade-offs and potential unintended consequences. [ABSTRACT FROM AUTHOR]

The advanced travel information system is a prominent intelligent transportation system for which variable message signs (VMSs) are the major components. In Iran, more attention has been paid to sign placement than to other components which are complementary to placement and combine to create relevant and effective messages for the driver. The current study used a stated preference survey to investigate the attitudes and reactions of drivers to VMS information that then were used to build models to reveal the reasons behind driver reactions to a VMS request to change route. The survey included variables affecting driver decisions, such as driver characteristics, attitude and perceptions of current VMS information and traffic in general. The influence of personality traits as subjective factors for driver behavior regarding route choice also was investigated. The results of the estimation of sequential hybrid binary logistic regression model showed that visual health had a significant positive effect on the driver decisions to divert to an alternate route when the VMS messages related to an accident or traffic congestion. Provision of expected delay information about the current route was found to be the most significant factor causing drivers to change route when a VMS indicated traffic congestion ahead. Personality traits such as Agreeableness, neuroticism, extraversion and openness significantly affected driver decisions about changing route when the VMS indicated traffic congestion or adverse weather conditions. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive framework for estimating passengers' transfer times and extracting their distribution and related transfer routes using WIFI probe data. The departure time of preceding station, arrival time of subsequent station, and train running time are selected to obtain transfer times. Then, the collected data is analyzed using kernel density estimation to obtain candidate distribution. Gaussian mixture models are adopted to extract the distribution of each possible transfer route at both peak hours and off-peak hours. This method is tested at two transfer stations of Xi'an metro system with the comparison of results from automatic fare collection data and manual sampling survey data. The results indicate that the proposed approach can collect the transfer time with a sampling ratio greater than 30% and a deviation less than 5%. The route choice behaviors and distribution of transfer time under various conditions can be identified using the proposed methods. [ABSTRACT FROM AUTHOR]

Comparison of routes recommended by online trip planners, such as Google Maps and MapQuest, with routes traveled by cyclists allows estimation of whether the routing criteria used in such trip planners are the same as those used by cyclists. This study uses GPS tracking data from the Endomondo fitness tracker app from trips in Miami-Dade (Florida) and North Holland and compares their characteristics to cycling routes suggested by Google Maps, MapQuest and the shortest path. Results highlight which trip attributes differ between Endomondo and its alternative trips, and how consistent those differences are across the two study areas. For example, Endomondo cyclists in both regions go through fewer traffic signals, and use more cycleways, footways, or bike lanes than suggested routes from Google and MapQuest. A multinomial logit model on observed Endomondo trips provides insight into the routing preferences of Endomondo commute and sport cyclists in both study regions. [ABSTRACT FROM AUTHOR]

Several systems have been proposed in recent years for Advanced Traveler Information Systems (ATIS), ranging from personal (mobile application, on-board) navigation to VMS (Variable Message Signs). All these applications are essentially aimed at assisting the travelers in their travel choices by means of technologically dispatched information about traffic network structure and traffic conditions. It is well known that in ATIS applications one of the key issues to be addressed concerns the accuracy of the dispatched information, here intended as the ability of the system in supplying information that can be verified by the travelers to be correct (affected by a small error). This paper aims at proposing some analytical models able to simulate the effects of the (in)accuracy on travelers' compliance with information. [ABSTRACT FROM AUTHOR]

The inclusion of human behavioral factors in travel demand analysis is a new trend in transportation planning. Perceived security (PS) is one of the crucial behavioral factors governing urban travelers' choices and demand analysis, exclusively for active modes of transportation like bicycles and vulnerable road users like women. However, no scientific attempt has been made to explain and quantify PS for women cyclists. We estimate women cyclists' PS by considering individual, social, and built environment features from the data collected in Tehran, Iran. Fifty-two women were recruited as the test participants in a VR-based bicycle experimental system (VRBES). For analysis, we focused on the tree-based machine learning (ML) methods besides an econometric model. In the end, the random forest (RF) algorithm is chosen and improved for the best performance (with test-R2=0.69) on the dataset, and feature importance showed population, a social feature, is the most important. [ABSTRACT FROM AUTHOR]

This paper concerns multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events. Five model architectures based on the multi-layer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), CNN-LSTM and Autoencoder LSTM networks have been developed to predict traffic flow under a road crash and the rain. Using an input dataset with five features (the flow rate, the speed, and the density, road incident and rainfall) and two standard metrics (the Root Mean Square error and the Mean Absolute error), models' performance is evaluated. [ABSTRACT FROM AUTHOR]

The development of techniques and systems for indicating routes in outdoor and indoor spaces has received a great deal of attention. While outdoor route planning is based on criteria such as the shortest, fastest route, and the least number of turns, indoor route planning is primarily focused on accessibility and safety. Routing systems for indoor spaces have become the focus of many researchers due to significant differences that make indoor routing is more complex than outdoor routing. An indoor environment is complex, which makes it difficult to find a route due to closed corridors, multiple floors, and other indoor features. Because of the present global scenario following the advent of the COVID-19 pandemic, indoor routing has become increasingly crucial. Wi-Fi, Bluetooth, and RFID are among the technologies used for indoor routing. For these technologies to help and guide users to the optimal route to their chosen destinations, they require accurate information, appropriate processing and modeling, and route density monitoring to ensure social distancing. In this paper, a new multi-user routing algorithm for indoor spaces is proposed. It has been adapted to the need for social distancing, and is based on multiple users, allowing more than one user to take the route or separate routes at one time without causing congestion on the same route. Despite the complexity of the system, the evaluation indicated that the proposed algorithm successfully controlled the flow of moving objects while ensuring social distancing and maintaining low costs. [ABSTRACT FROM AUTHOR]

Multinomial logit model (MNL) is by far the most widely used discrete choice model that is widely used to explain or predict a choice from a set of two or more discrete alternatives. MNL operates within a framework of the random utility model (RUM) in which the utility of an alternative perceived by an individual consists of two components: systematic component and random component. The systematic component is usually defined as a linear function. However, practical decision processes involve complex considerations regarding various aspects of the alternatives and individual which cannot be adequately represented by simple linear models. To overcome the weakness of linear utility model and improve the performance of MNL, in this paper, we propose a general deep multinomial logit model (GDMNL) that takes advantage of both traditional MNL and deep learning. In this model, deep neural networks are applied to extend MNL by learning different nonlinear utility functions of various alternatives. The empirical study in the domain of transit route choice analysis demonstrates the validity and superiority of the proposed model. [ABSTRACT FROM AUTHOR]

The environmental traffic assignment problem in the bimodal network with electric vehicles (EVs) and gasoline vehicles (GVs) has become a hot topic recently. However, few previous works consider the psychological difference between EV users and GV users in terms of environmental awareness , in order to fill in this research gap, we formulate an environment-oriented mixed-behavior user equilibrium (EMUE) model to distinguish the route choice criterion between EV users and GV users by considering the Pro-environmental behavior (PEB) of EV users.. We formulate a bi-objective optimization model and propose a differentiable road pricing scheme for EV and GV users to manage the congestion and emissions simultaneously. We investigate whether the differentiable road pricing can decentralize the Pareto-efficient flow as a unique mixed-behavior equilibrium. Moreover, we conduct numerical experiments based on the Sioux Falls network and the existing urban network in Qianhai Zone to evaluate the effectiveness of the proposed pricing scheme, models and algorithms. [ABSTRACT FROM AUTHOR]

Most choice models, e.g. Multinomial Logit (MNL), rely on random utility theory, which assumes that a compensatory utility maximization decision rule explains an individual's choice behaviour. Research has shown, however, that behaviour is sometimes better explained by non-compensatory decision rules. While some research has used Latent Class Choice Models (LCCMs) to account for multiple decision rules, many of them – such as the disjunctive rule – have yet to be explored. This paper formulates, estimates, and evaluates a LCCM that combines the MNL with a Generalised Random Disjunctive Model (GRDM), a new choice model we develop. Addressing deficiencies of existing disjunctive choice models, the GRDM allows for relative importance between attributes and is insensitive to irrelevant attributes. Unlike most non-compensatory models, it is tractable and incorporates random error terms for capturing unobserved heterogeneity across choice situations. The GRDM can be expressed as a Universal Logit (UL) model, which helps derive welfare metrics such as Marginal Rates of Substitution and elasticities and makes it possible to estimate the model with traditional software packages. The LCCM combining the GRDM and the MNL is estimated in two large-scale case studies: cyclists' route choice and public transport route choice. Results are compared with other relevant LCCM specifications and the individual choice models, where it is found that the MNL + GRDM LCCM provides the best fit to the data. We also interpret the fitted parameters and calculate the Marginal Rates of Substitution, which align with behavioural expectations. • Formulation of the new Generalised Random Disjunctive Model (GRDM). • Derivation and interpretation of the GRDM Marginal Rates of Substitution and Logsum. • Motivation for accounting for decision rule heterogeneity in choice models. • Presentation of a model combining the GRDM and a MNL, using a Latent Class framework. • Benchmark and interpretation of the new models on two route choice case studies. [ABSTRACT FROM AUTHOR]

Despite the recent advances in text classification and the performance improvement yielded by Transformers models, the absence or inaccessibility of an adequate dataset to train a text classifier motivates the choice for alternative routes. In this study, the need to detect specific topics in the news and to discard irrelevant content encouraged the development of an article tagging pipeline which assesses the similarity between a user-defined dictionary of topic-specific keywords and news article keywords. The innovation of the paper stands in the exploitation of two BERT-based algorithms to retrieve article keywords and to embed them, which previous studies have shown to outperform state of the art solutions for keywords extraction and semantic textual similarity. In a nutshell, the pipeline computes the semantic similarity between the sentence embeddings generated from topic-specific keywords and those produced from news article keywords extracted with the KeyBERT algorithm, finally classifying each article according to a previously defined topic. The results are supported by sound coherence and diversity metrics computed, by aggregating each article by their first tag, which attests to the semantic validity of the pipeline outputs. [ABSTRACT FROM AUTHOR]

Interchange provides more flexibility in route choice, a key travel behaviour in urban rail transit, but its influence is usually simplified. This paper investigates how interchange affects route choice with passenger perception considered. At single-interchange level, perceived interchange time was proposed and modelled under three resolutions to capture passenger perception and its sensitivity. At route level, the influence of interchange was modeled by first comparing eight quantifications of interchange. Mixed logit models with the best interchange proxy were further developed to address the correlation among alternative routes and reveal the potential taste variations among passengers. Results based on Shanghai Metro data showed perceived interchange time, including passenger perception and interchange environment, better represents the influence of interchange in route choice, meanwhile the weights of interchanges on one route rise sequentially and non-linearly. The results can improve route choice prediction in demand modelling and route recommendation in advanced traveller information systems. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are environment friendly and are expected to find tremendous growth in the near future. However, the short voyaging reach and the lack of charging lattice have impeded the large-scale application of EVs. A choice of a particular route for EV is largely dependent on the availability of charging stations on that route as well as the ease of charging facility offered by the charging station (including state of the charging station, user convenience, etc.). Hence, it is vital to delve into the challenge of locating a charging station that offers the utmost user convenience in charging EVs. This paper attempts to present a solution to this very problem. The proposed approach is an improvised distributed system, namely, S 2 RC (Smart Search of Route and Charging) which plans an energy efficient EV route considering EV's state-of-charge (SoC) level, traffic conditions, the frequency of charging stations and the state of charging station (the charging resources of the charging station along with their occupancy levels, etc.). This distributed architecture employs the proposed agile charging slot reservation approach for the EVs that wish to get recharged at a particular charging station. Besides, to delineate the interactive mechanism of EVs recharging, three non-identical objective functions are formulated to minimize the overall energy consumption of EVs, waiting time at charging station, and total charging expenditure. After performing the extensive simulations on the weighted directed real transportation graph of Chandigarh, India, the proposed S 2 RC system recommends a charging station in accordance to the preferences given by the EV user. Case studies verify the significance and validity of the proposed model in terms of energy efficiency and user convenience. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In travel choice models, variables describing alternative attributes such as travel time may have to be specified as stochastic because the analyst may not have accurate measurements of the attribute values considered by the decision-maker. Such stochasticity in alternative attributes is different from unobserved heterogeneity in the coefficients representing travellers' response to those attributes. Specifying only one of these as random while keeping the other fixed can potentially result in biased parameter estimates, inferior goodness-of-fit, and distorted information for policy analysis. Therefore, in this study, we propose an integrated choice and stochastic variable modelling framework with random coefficients (i.e., an ICSV-RC framework) that allows the analyst to accommodate stochasticity in alternative attributes and random coefficients on such attributes. In addition, we show that ignoring either source of stochasticity – stochasticity in alternative attributes or unobserved heterogeneity in response to the attributes – results in models with inferior goodness-of-fit and a systematic bias in all parameter estimates. We demonstrate this using simulation experiments for two different travel choice settings, one involving labelled mode choice alternatives and the other involving unlabelled route choice alternatives. In addition, we present an empirical analysis in the context of truck route choice to highlight the importance of accommodating both sources of variability – stochasticity in travel times and random heterogeneity in response to travel times. • In choice models, it may be important to recognize stochasticity in alternative attributes along with random coefficients on those attributes. • We propose integrated choice and stochastic variable models that accommodate stochastic variables and random coefficients on them. • Ignoring either source of stochasticity leads to systematic bias in estimation and distorted willingness to pay measures. • Simulation experiments and an empirical application support these hypotheses in the context of travel time and response to travel time. [ABSTRACT FROM AUTHOR]

It is sometimes considered that there are 2 routes to moral choice, deontological and utilitarian (with debate as to whether each of these routes uniquely reflect emotional vs. analytic components). Yet in moral judgments we are often faced with a third route to choice, based on rules imposed by an outside agency (most typically the legal framework of a country). Whether we agree with these rules or not, we can choose to guide moral choice in this way. Moreover, depending on the situation, we can sometimes choose to prioritize such external rules or not. Sometimes external rules coincide with either deontological or utilitarian biases. But what happens when they do not? Using a novel paradigm for moral judgment, the refugee dilemma, we provide evidence for such a route to moral choice based on external rules. We show in 3 experiments that participants with high scores on a cognitive reflection test or under cognitive load were more likely to adopt utilitarian or rule-based responses, as opposed to emotional ones. We also found that rule-based respondents reported the highest average psychological distance. These findings extend the predominant approach of dual process models. [ABSTRACT FROM AUTHOR]

MATSim is an agent-based transport simulation model. In contrast to a pure dynamic traffic assignment (DTA) model, MATSim can react to more choice dimensions than route choice, which is the only choice treated by a typical DTA. Simulating the mobility and activity participation of individuals during the whole day, MATSim can additionally represent mode choice, departure time choice, and other decisions and is, therefore, policy-sensitive in terms of these choice dimensions. This allows for the analysis of a wide scope of policies. MATSim can, however, not model the choice of the sequence of activity participation nor the choice of activity participation as such. Also, choices of locations are not typically part of the modeling scope. Interventions into the transport and land-use systems may, however, be substantial such that they can effect changes in behavior in terms of these choices. To allow for the assessment of such reactions, the FEATHERS activity-based demand model is coupled with MATSim. This paper explores different options of integration and describes the development steps of the integration of FEATHERS and MATSim. [ABSTRACT FROM AUTHOR]

In shared space, all road users are encouraged to legally occupy the same road space with little physical or visual separation. Complex social interactions that appear in these spaces create complicated ecosystems, which make the estimation of future impacts difficult using classic modeling techniques. This study introduces a concept Agent-Based Simulation Model to represent this complex ecosystem at a network level. The proposed model is based on the methodological framework of Multi-Agent Transport Simulation (MATSim). Two critical factors are integrated in this iterative simulation process, i.e. traffic complexity level that influences network supply and perceived safety that affects mode and route choices. [ABSTRACT FROM AUTHOR]

Used for route choice modelling by the transportation research community, recursive logit is a form of inverse reinforcement learning. By solving a large-scale system of linear equations recursive logit allows estimation of an optimal (negative) reward function in a computationally efficient way that performs for large networks and a large number of observations. In this paper we review examples of recursive logit and inverse reinforcement learning models applied to real world GPS travel trajectories and explore some of the challenges in modeling bicycle route choice in the city of Amsterdam using recursive logit as compared to a simple baseline multinomial logit model with environmental variables. We discuss conceptual, computational, numerical and statistical issues that we encountered and conclude with recommendation for further research. [ABSTRACT FROM AUTHOR]

Travel time information assists road users in making informed travel decisions such as mode choice, route choice and/or time of travel. This study explores the use of GPS data from buses and Wi-Fi and Bluetooth data from a sample of vehicles, for accurate estimation of the travel time of all vehicles on the roadway. A 5.5 km road stretch in Chennai city was selected as study stretch and data were collected for a week's period. The present study develops models using linear regression and artificial neural network (ANN) techniquesFto estimate stream travel time using bus travel time obtained from GPS. ANN performed better compared to the linear regression for all sizes of segments. Most of the Indian cities have an integrated network of buses traveling on most of the road segments with on-board tracking devices, making this a useful development for real-time travel time estimation. [ABSTRACT FROM AUTHOR]

In this paper, we revisit the concept of Prospect-based User Equilibrium in a dynamic context. We consider a mesoscopic Lighthill-Whitham-Richards (LWR) traffic model to determine time-dependent route costs. We propose a solution algorithm to determine the network equilibria. Monte Carlo simulations are used to account for the travel time distributions. We analyze the dynamic Prospect-based User Equilibrium compared to the benchmarks Deterministic and Stochastic User Equilibrium, on a synthetic Manhattan network. We set four endogenous reference points. We show that the setting of the reference point plays a very important role in the route flow patterns and on the network performance at an aggregated level, i.e. in terms of vehicles mean speed as well as internal and outflow capacities. Our results also enhance that the Prospect-based User Equilibrium is more sensitive to a change in the reference point than in the calibration of the users' risk-aversion and risk-seeking parameters. [ABSTRACT FROM AUTHOR]

Spatial experiences in route finding, such as the ability of finding low‐traffic routes, exert a significant influence on travel time in big cities; therefore, the spatial experiences of seasoned individuals such as taxi drivers in route finding can be useful for improving route‐finding algorithms and preventing using routes having considerable traffic. In this regard, a spatial experience‐based route‐finding algorithm is introduced through ontology in this paper. To this end, different methods of modeling experiences are investigated. Then, a modeling method is chosen for modeling the experiences of drivers for route finding depending on the advantages of ontology, and an ontology based on the taxi drivers' experiences is proposed. This ontology is employed to create an ontology‐based route‐finding algorithm. The results are compared with those of Google maps in terms of route length and travel time at peak traffic time. According to the results, although the route lengths of route‐finding method based on the ontology of drivers' experiences in three cases (from nine cases) are greater than that based on Google maps, the travel times are shorter in most cases, and in some routes, the difference in travel time reaches only 10 minutes. [ABSTRACT FROM AUTHOR]

Gastric adenocarcinoma (GA) is the 5th most common cancer in the world; in France, however, its incidence has been steadily decreasing. Twenty-five experts brought together under the aegis of the French Association of Surgery collaborated in the drafting of a series of recommendations for surgical management of GA. As concerns preoperative evaluation and work-up, echo-endoscopy aimed at clarifying lymph node status should be performed in all candidates for surgical resection and exploratory laparoscopy in cases of GA cT3/T4 and/or N+ for peritoneal carcinomatosis. On the other hand, PET-scan should not be performed systematically, but only when the other modalities for diagnosis prove insufficient. Laparotomy remains the route of choice to achieve total or partial gastrectomy with D2 lymph node lymphadenectomy for advanced lesions (> T2N0). To limit the risk of dumping syndrome and esophageal reflux and as a way of reestablishing continuity, construction of a jejunal pouch on Roux-en-Y following total gastrectomy is recommended. In cases of peritoneal carcinosis in GA with a low peritoneal cancer index (PCI) (< 7) in a patient in good general condition whose disease is controlled by chemotherapy, macroscopically complete cytoreduction with intraperitoneal hyperthermal chemotherapy will probably be required, and it will have to take place in an expert center. Only in the event of Child A cirrhosis may gastrectomy with D2 lymphadenectomy be considered. Palliative gastrectomy or surgical bypass for distal stomach obstruction in a patient in good general condition may also be envisioned. [ABSTRACT FROM AUTHOR]

Modelling realistic public transport route choices is a key element for transport planning tools. Although these decisions have been modeled based on travel time and travel cost, the emergence of new public means of transport makes the problem more challenging. In this work, we extend the public transport router of a widely used mobility simulation platform called MATSim. Besides fixed scheduled services, the proposed router is able to include on-demand shared services as well as private door-to-door services. With the co-evolutionary algorithm of MATSim, this router records waiting and travel times from previous iterations to improve public transport routes. This allows to take into account interactions between agents when routes are decided. The approach is tested in a model of a future neighborhood of Singapore, where on-demand services complement the existing fixed schedule supply. [ABSTRACT FROM AUTHOR]

Despite requiring less space than most other modes of transport, bicycle traffic will also be prone to congestion when the traffic volume is sufficiently large. Such congestion can eventually influence the route choices of cyclists using the network. In this study we model bicycle congestion on a detailed network of the greater Copenhagen area by assigning an entire day of bicycle traffic using a recently developed method for dynamic network loading of speed heterogeneous multi-lane bicycle traffic. The model iteratively assigns appropriate routes for more than a million bicycle trips in the demand sensitive network, and with computation times of less than 15 minutes per iteration the proposed model proves to be large-scale applicable. This makes it the first dedicated bicycle traffic assignment model to account for congestion. The results indicate that the solid bicycle infrastructure of Copenhagen and cyclists' willingness to change routes are key to keeping travel times low for cyclists. [ABSTRACT FROM AUTHOR]

Microsimulation of travel flows aims to assess the effect of decisions taken by travelers based on personal preferences, time-of-day, properties of the infrastructure and expected or perceived travel flows. Route choice represents a particular class of such decisions. Route choice prediction is an essential component of microsimulators. Specification of choice models and estimation of the corresponding parameters based on observations are required in the preparatory stage. Route choice sets need to be established for sampling in the simulation stage. This paper is part of a research project aiming to investigate how route complexity can be integrated in the choice process modeling. In particular routes for bikers collected by GPS tracking in the Dutch FietsTelWeek project in 2016 are analyzed. The data exploration stage and the research project outline are covered. Properties of the publicly available fietstelweek2016 dataset used for model training are investigated in order to assess their effect on prediction results. In order to achieve the project goal, the research project structure is briefly discussed. It is based on the observation that the number of routes recorded for each OD-pair is too small to observe a frequency distribution for complexity. Hence, complexity data are collected for sub-networks that are similar with respect to particular graph properties. [ABSTRACT FROM AUTHOR]

Everyday route choices made by bicyclists are known to be more difficult to explain than vehicle routes, yet prediction of these choices is essential for guiding infrastructural investment in safe cycling. In this paper we study how the concept of route complexity can help generate and analyze plausible choice sets in the demand modeling process. The complexity of a given path in a graph is the minimum number of shortest paths that is required to specify that path. Complexity is a path attribute which is considered to be important for route choice in a similar way as the number of left turns, the number of speed bumps, distance and other. The complexity was determined for a large set of observed routes and for routes in the generated choice sets for the corresponding origin-destination pairs. The respective distributions seem to significantly differ so that the choice sets do not reflect the traveler preferences. This paper looks at how the observed routes compare to routes generated by Breadth First Search Link Elimination and Double Stochastic Generation Function method. [ABSTRACT FROM AUTHOR]

In public transport disruptions, a key relation between the services of operating company and passengers' satisfaction is the disseminated information. This paper focus on how the issue time of information affects passengers in the unplanned/unexpected public transport disruptions. Due to different information issue time, passengers' behaviours are assumed and simulated. An agent-based micro-simulation model (MATSim) is applied for the city of Zürich, Switzerland, for the benefit of heterogeneous activity-based simulation in a multi-modal network. We develop a novel within-day replanning approach in MATSim, to study agents' route choices responding to public transport disruptions in one single iteration. The disruption is assumed as a rail track blockage between Zürich HB and Zürich Oerlikon via both Zürich Wipkingen and Zürich Hardbrücke. Statistic results are analysed for all the agents who may be involved in the defined disruption. Agents' travel chain shows their adaptations to the corresponding information scenarios. Agents' scores reveal that the issue time of information significantly impacts agents' satisfaction in public transport disruption. In other terms, an investment in better information might be complementary and comparable in an investment in a better management of the operations during the disruption, in interconnected multimodal networks. [ABSTRACT FROM AUTHOR]

Public transit is a backbone for well-functioning cities, forming a complicated system of interconnecting lines each with their own frequency. Defining accessibility for public transit is just as complicated, as travel times can change every minute depending on location and departure time. With Pareto optimal journeys it is possible to look beyond the earliest arrival times and also optimize for the shortest travel time, as travellers base their departure time on the start time given by their smartphone app, especially when service frequencies are low. By querying for all Pareto optimal journeys in a time range it becomes possible to get a grasp of what passengers see as their choice set when it comes to transit route choice. Based on the averages of the Pareto optimal journeys it should become possible to calculate more realistic skim matrices for traffic analysis zones, including reliability factors such as frequencies and the number of transfers. In this study we calculate Pareto optimal journeys in the area in and around Amsterdam, looking at how travel times are distributed and what factors impact them. [ABSTRACT FROM AUTHOR]

Advance Traveler Information Systems (ATIS) inform drivers about traffic incidences and expected travel times/ delays en-route. An online computer study was conducted in Qatar to investigate drivers' willingness to divert to an alternative route given changes in expected travel conditions. Respondents' route choices were queried after exposure for 6 seconds to varying display strategies. The results from a binary logistic regression and a stated preference survey showed that delay times and displayed colors on a Graphical Route Information Panel (GRIP) effectively influence drivers to take the alternative route, while total travel times were preferred for Variable Message Signs (VMS). [ABSTRACT FROM AUTHOR]

Apart from daily recurrent traffic congestion, unforeseen events such as flood induced road damages or bridge collapses can degrade the capacity of traffic supply and cause a significant influence on travel demand. An individual realising the unexpected events would take action to reschedule its day plan in order to fit into the new circumstance. This paper analyses the potential reschedule possibilities by augmenting the Within-Day Replanning simulation model implemented in the Multi-Agent Transport Simulation (MATSim) framework. Agents can adjust day plan through multi-dimensional travel decisions including route choice, departure time choice, mode switch, and trip cancellation. The enhanced model not only improves the flexibility of MATSim in rescheduling a plan during an execution day, but also lays the foundation of integrating more detailed heterogeneity decision rules into the travel behaviour simulation to cope with unexpected incidents. Furthermore, the proposed rescheduling model is capable of predicting the network performance in the real-world picture and gives a hint on how best react to transport disruptions for transport management agency. [ABSTRACT FROM AUTHOR]

Route choice models are a vital tool for evaluating the impact of transportation policies and infrastructure improvements, such as the addition of new roads, tolls, or congestion charges. They can also be used to predict traffic flow and congestion levels, which is essential for traffic management and control. The aim of this manuscript is to provide a comprehensive analysis of the effectiveness and limitations of various game theory (GT) based models used in route choice modelling. The manuscript draws upon the theoretical foundations of game theory to explore the complex decision-making processes of travelers in transportation networks, focusing on factors such as travel time, congestion. The manuscript discusses the challenges and opportunities associated with implementing game theory-based models in practice, including the data requirements, model calibration, and computational complexity. These factors are considered in relation to the suitability of different game theory-based models, including cooperative, non-cooperative, and evolutionary games. The comparative critiques presented in this manuscript provide guidance for future research directions in the field of private route choice modelling, aimed at academic researchers, engineers, policymakers, and industrial communities. • A cutting-edge review of the application of game theory in route choice modelling. • Effectiveness and limitations of the different GT-based models of route choice. • Challenges and opportunities associated with implementing GT-based models of route choice in practice. • Future research directions for scholars, engineers, policymakers, and industrial communities. [ABSTRACT FROM AUTHOR]

The emergence of modern tools and technologies gives a unique opportunity to collect large amounts of data for understanding behaviour. However, the generated datasets are often imbalanced, as individuals might contribute to the datasets at different frequencies and periods. Models based on these datasets are challenging to estimate, and the results are not straightforward to interpret without considering the sample structure. This study investigates the issue of handling imbalanced panel datasets for modelling individual behaviour. It first conducts a simulation experiment to study to which degree mixed logit models with and without panel reproduce the population preferences when using imbalanced data. It then investigates how the application of bias reduction strategies, such as subsampling and likelihood weighting, influences model results and finds that combining these techniques helps to find an optimal trade-off between bias and variance of the estimates. Considering the conclusions from the simulation study, a large-scale case study estimates bicycle route choice models with different correction strategies. These strategies are compared in terms of efficiency, weighted fit measures, and computational burden to provide recommendations that fit the modelling purpose. We find that the weighted panel mixed multinomial logit model, estimated on the entire dataset, performs best in terms of minimising the bias-efficiency trade-off in the estimates. Finally, we propose a strategy that ensures equal contribution of each individual to the estimation results, regardless of their representation in the sample, while reducing the computational burden related to estimating models on large datasets. • Handling of a panel dataset with imbalanced number of observations per individual. • There exists a bias-efficiency trade-off when sampling from imbalanced panel data. • Weighted panel MXL model on the whole dataset performs best w.r.t. the trade-off. • Providing mathematical reformulations for estimating a model on large-scale data. • Estimating a model that ensures equal contribution of each individual to the results. [ABSTRACT FROM AUTHOR]

To study the mechanism of pedestrians' route choice behaviors under non-emergency conditions, we conducted a series of route choice experiments. Participants were required to choose between two routes. Possible controls, including bottleneck, social distancing, extra reward, were tested in the experiments. Results shows that the bottleneck effect can dramatically influence the route-choice behaviors, whereas the impact of social distancing and reward were much weaker. Five typical logit models, including Binary Logit (BL) model, Mixed Logit (ML) model, Panel Logit (PL) model, Latent Class Logit (LCL) model and Latent Class Logit including Panel effect (LCL-P) model were employed. PL and LCL models performed better in this study, while the results of LCL-P model were the best. This suggests the existence and importance of heterogeneity in route choice behavior. Two classes of pedestrians were identified, with one being comfort-seeking and the other being speed-seeking. ML model did not perform well in this study, which is contrary to some previous studies. All these results could be helpful for understanding the essence of pedestrians' route choice behaviors. • We studied pedestrians' route choice under crowded and non-emergency conditions. • Travel time is not always negatively associated with route choice probability. • Two latent classes, including comfort-seeking and speed-seeking, are observed. • The performance of mixed logit model is not satisfied. • A new model considers both latent class and panel effect performs best. [ABSTRACT FROM AUTHOR]

The article deals with the organization of project training within the university education through the construction of individual educational routes. The authors believe that the value orientationsand the motivationsystem are not only interrelated, but also mutually influence each other, which confirms the need to study value orientations when building an individual route. Based on the Milton Rokich questionnaire, the authors identify three groups of students based on their value orientations. The stages of building individual educational routes are also presented. [ABSTRACT FROM AUTHOR]

Copyright of Avances is the property of Instituto de Informacion Cientifica y Tecnologica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we are investigating the two-stage supply chain problem with fixed costs associated to the routes and propose an efficient heuristic algorithm for the minimization of the total transportation costs. Our novel heuristic algorithm is constructed to fit the challenges of the addressed supply chain optimization problem and starts with building several initial solutions by processing customers in a specific order and choosing the best available supply route for each customer. After each initial solution is built, a process of searching for better variants around that solution follows, restricting the way the transportation routes are chosen. We have performed computational experiments on two sets of instances: one that contains 20 benchmark instances existing in the literature and a second one that consists of 10 new randomly generated instances. The obtained computational results show that our proposed heuristic algorithm is highly competitive in comparison with the existing solution approaches from the literature. [ABSTRACT FROM AUTHOR]

Microsimulation of travel flows aims to assess the effect of decisions taken by travelers based on personal preferences, time-of-day, properties of the infrastructure and expected or perceived travel flows. Route choice represents a particular class of such decisions. Route choice prediction is an essential component of microsimulators. Specification of choice models and estimation of the corresponding parameters based on observations are required in the preparatory stage. Route choice sets need to be established for sampling in the simulation stage. This paper is part of a research project aiming to investigate how route complexity can be integrated in the choice process modeling. In particular routes for bikers collected by GPS tracking in the Dutch FietsTelWeek project in 2016 are analyzed. The data exploration stage and the research project outline are covered. Properties of the publicly available fietstelweek2016 dataset used for model training are investigated in order to assess their effect on prediction results. In order to achieve the project goal, the research project structure is briefly discussed. It is based on the observation that the number of routes recorded for each OD-pair is too small to observe a frequency distribution for complexity. Hence, complexity data are collected for sub-networks that are similar with respect to particular graph properties. [ABSTRACT FROM AUTHOR]

Modelling realistic public transport route choices is a key element for transport planning tools. Although these decisions have been modeled based on travel time and travel cost, the emergence of new public means of transport makes the problem more challenging. In this work, we extend the public transport router of a widely used mobility simulation platform called MATSim. Besides fixed scheduled services, the proposed router is able to include on-demand shared services as well as private door-to-door services. With the co-evolutionary algorithm of MATSim, this router records waiting and travel times from previous iterations to improve public transport routes. This allows to take into account interactions between agents when routes are decided. The approach is tested in a model of a future neighborhood of Singapore, where on-demand services complement the existing fixed schedule supply. [ABSTRACT FROM AUTHOR]

Despite requiring less space than most other modes of transport, bicycle traffic will also be prone to congestion when the traffic volume is sufficiently large. Such congestion can eventually influence the route choices of cyclists using the network. In this study we model bicycle congestion on a detailed network of the greater Copenhagen area by assigning an entire day of bicycle traffic using a recently developed method for dynamic network loading of speed heterogeneous multi-lane bicycle traffic. The model iteratively assigns appropriate routes for more than a million bicycle trips in the demand sensitive network, and with computation times of less than 15 minutes per iteration the proposed model proves to be large-scale applicable. This makes it the first dedicated bicycle traffic assignment model to account for congestion. The results indicate that the solid bicycle infrastructure of Copenhagen and cyclists' willingness to change routes are key to keeping travel times low for cyclists. [ABSTRACT FROM AUTHOR]

Everyday route choices made by bicyclists are known to be more difficult to explain than vehicle routes, yet prediction of these choices is essential for guiding infrastructural investment in safe cycling. In this paper we study how the concept of route complexity can help generate and analyze plausible choice sets in the demand modeling process. The complexity of a given path in a graph is the minimum number of shortest paths that is required to specify that path. Complexity is a path attribute which is considered to be important for route choice in a similar way as the number of left turns, the number of speed bumps, distance and other. The complexity was determined for a large set of observed routes and for routes in the generated choice sets for the corresponding origin-destination pairs. The respective distributions seem to significantly differ so that the choice sets do not reflect the traveler preferences. This paper looks at how the observed routes compare to routes generated by Breadth First Search Link Elimination and Double Stochastic Generation Function method. [ABSTRACT FROM AUTHOR]

In view of the spare parts supply route problem with hard time windows in transportation, an optimal model that aims to minimize the total delivery time is put forward. In this paper, the supply route optimization problem with the shortest transportation distance as the decision target is discussed in detail. Taking the shortest total transportation distance of all vehicles as the optimization goal, a mathematical analytical model that considers the hard time window requirements of each customer is established. Based on the improved ant colony algorithm, the algorithm flow of solving the vehicle routing problem with hard time windows is designed to solve the difficulty of the model. In addition, the feasibility of the improved algorithm is verified by a case considering the actual terrain. The results show that the improved algorithm can quickly find the solution of VRPTW and provide an effective delivery plan for decision makers. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between the built environment and walking behavior for socially resilient urban design to promote walking in the aging population. We used a total of 117,259 GPS points and 160 person-day data points for elderly individuals living in Uijeongbu-si, Gyeonggi-do, Korea. The differences in the walking environments selected based on the walking purpose were analyzed using a random forest method, and the nonlinear relationship was investigated. In addition, an in-depth analysis was performed by adopting an interpretation approach from global and local perspectives. The findings highlight the significance of walking street pleasure and network properties for leisure walking. Specifically, a green area ratio of 15 % or more positively relationship path choice for leisure, underscoring the importance of sufficient green space. Conversely, the high proportion of green areas typically has negative implications for commercial walking. Road connectivity and integration are predominantly negatively associated with route choice for leisure, indicating a decreased preference for highly integrated roads during leisure walking activities. These findings provide valuable insights for an evidence-based urban design strategy to make walkable environments. It can contribute to urban planning and policy, particularly the development of tailored walkable environments for the older population. [ABSTRACT FROM AUTHOR]