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This paper investigates the performance – in terms of choice probabilities and correlations – of existing and new specifications of closed-form route choice models with flexible correlation patterns, namely the Link Nested Logit (LNL), the Paired Combinatorial Logit (PCL) and the more recent Combination of Nested Logit (CoNL) models. Following a consolidated track in the literature, choice probabilities and correlations of the Multinomial Probit (MNP) model are taken as targets. Laboratory experiments on small/medium-size networks are illustrated, also leveraging a procedure for practical calculation of correlations of any GEV models. Results show that models with inherent limitations in the coverage of the domain of feasible correlations yield unsatisfactory performance, whilst the specifications of the CoNL proposed in the paper appear the best in fitting both MNP correlations and probabilities. The performance of the models is appreciably ameliorated by introducing lower bounds to the nesting parameters. Overall, the paper provides guides for the practical application of tested models.

The constant evolution of many urban areas ultimately reaches a point where the current infrastructure cannot further serve the needs of citizens. In the case of transport networks, congested roads, increased delay, and low level of service are among the indicators of a need for road infrastructure upgrade. Thessaloniki is the second-largest city in Greece with a population of over 1 million inhabitants in its metropolitan area. Currently, a significant share of the city's traffic demand is served via its ring road, whose capacity is set to be enhanced through the construction of a flyover highway with the simultaneous upgrade of the existing ring road. The current study aims at investigating the key factors determining the final route choice of drivers between the two road axes. To that end, data from a combined revealed and stated preference survey targeting car drivers were collected, which were later exploited as the basis for the development of binary route choice regression and machine learning models. The results reveal that drivers' choice is affected by criteria such as total travel time, the probability of accident occurrence, and closure time due to accident. The results of this paper could prove beneficial to transport researchers in forecasting drivers' behavior in terms of route choice and to practitioners during the planning phase of similar infrastructure projects. [ABSTRACT FROM AUTHOR]

Path inference aims to reveal missing paths given a few number of GPS samples associated with a moving object by exploiting the topology of road network and statistical information of historical GPS trajectories, and plays a vital role in data preprocessing of location based information services. But, in practice path inference severely suffers from the data sparsity as well as the randomness of drivers path selection behaviors. In this paper, we propose a novel Bayesian path inference model subject to spatiotemporal constraints by taking into account the drivers path selection behaviors. To be specific, the problem of path inference is cast as the problem of searching K most probable candidate paths according to the joint posterior selection probabilities of candidate paths. When estimating model parameters, we use the frequency of each road segment in the historical GPS trajectories instead of that of road segment transfers to mitigate the influence of data sparsity. In addition, both spatiotemporal constraints and probability thresholds are introduced to narrow the search space, which significantly improves the time efficiency. The experiments are conducted using practical data and show that the proposed model is significantly superior to three existing popular models. When the GPS sampling interval varies from 1 minute to 5 minutes, the accuracy of the proposed method is 0.94, 0.91, 0.86, 0.80 and 0.74, and the Jaccard similarity 0.89, 0.85, 0.83, 0.80 and 0.75 respectively, the average improvement in accuracy rises from 3.68% to 18.69% and that in the Jaccard similarity from 4.56% to 18.42%. [ABSTRACT FROM AUTHOR]

Most of the existing map matching methods are developed considering global positioning system (GPS) data. However, the limitations of GPS data, such as a small sample size and presence of position errors, impede the application of such data in intelligent transportation system (ITS) applications. In contrast, automatic vehicle identification (AVI) data exhibit unique advantages and can overcome the issues related to the GPS data. The existing map matching methods are unsuitable for use with AVI data due to the sparsity issue. Therefore, in this study, a map matching method named AVI-MM is developed that is customized for sparse AVI data. The AVI-MM first decomposes the AVI trajectories with the connective observation pairs. Subsequently, the set of candidate sub-paths for each sensor pair is generated, and each candidate sub-path is assigned a matching probability. Finally, the matching path is determined by connecting the sub-path with the highest matching probability. As many sub-paths can connect the sparse AVI observations, a candidate set generation algorithm is developed to generate a sufficient number of feasible and attractive candidates. Moreover, to reliably and accurately determine the true path, the matching probability is defined based on a spatial-temporal analysis and drivers’ route choice behaviour analysis. Field-test data are used to estimate and evaluate the proposed method. Compared with three benchmark methods, the proposed AVI-MM exhibits a significantly enhanced matching accuracy without a considerable loss in the computation efficiency. Moreover, the evaluation results of the robustness of the proposed algorithm suggest that the algorithm can correctly identify 82.55% of the links when the spatial gap between the observation pair is less than 20 km. Specifically, the proposed method exhibits a satisfactory performance for 84.02% of the observed samples in the test dataset. [ABSTRACT FROM AUTHOR]

Travelers make different choices about their routes based on the perceived travel time. This study aims to analyze the route choice behaviors by grouping them according to their perceived travel times. Reliability preference is a behavioral characteristic for travelers' on-time arrival according to stochastic travel time, and it is used to classify the travelers into four groups. From Dedicated Short Range Communication (DSRC) data in Daegu, 9,758 travelers are observed to analyze their route choice behaviors. A stochastic model with generated sets of choices is developed using the probabilistic characteristics of travel times. A route-choice model is also estimated for each group, and the travelers' route-choice behaviors are analyzed using a mixed path-size correction logit (MPSCL) model. The statistical analysis indicates that there are different route-choice behaviors among the four groups. The different behaviors concerning the routes are analyzed by comparing the magnitude of the different coefficients. This study contributes to constructing different strategies for acquiring information considering the route-choice behaviors of each group. Providing travel information that considers individual preferences in travel choice is essential in smart mobility transportation systems. [ABSTRACT FROM AUTHOR]

Recent studies adopted models of user acceptance of information technology to predict and explain drivers’ acceptance of traffic information. Among these frameworks, the most commonly used is the Technology Acceptance Model (TAM). However, TAM is too general and does not consider drivers’ response in specific traffic conditions or choice scenarios. This study combines an extended TAM with different choice scenarios displayed by Variable Message Signs (VMS) into a Hybrid Choice Model (HCM). Two models are proposed. The first model takes into account the causal relationships among latent variables based on the following hypotheses: Information Quality (IQ) has a positive effect on Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) which, in turn, have a positive effect on the Behavioural Intention (BI) to use traffic information. In the second model, the four latent variables PU, PEOU, IQ, and BI are directly added to the utility function without any causal relationships. 339 drivers with valid licence were interviewed via Stated Preference (SP) survey and the results show that TAM can explain travellers’ response to VMS if the causal relationships among latent variables are taken into account. In addition, all hypothesized relationships are strongly supported. Practical and academic implications are also discussed. First published online 27 April 2020

To better comprehend the relationship between the environment and walking, this study developed a conceptual framework that explained the association between the street environment and the route choice behavior of pedestrians. We collected the route choice data of 219 residents of the Chunliu community in Dalian and used a conditional Logit model to analyze the factors influencing route choice behavior to explain how the street environment affected pedestrians' walking habits and induced them to choose longer or more complicated routes for their activities. We found that sidewalk and driveway width, garbage bins, green spaces, the characteristics of street walls, the proportion of facilities could influence pedestrians' walking habits and compel them to choose longer and more complex routes. This study would provide new insights into walking characteristics and offer policy recommendations to the government on improving the street environment.

Taxi sharing is becoming a trendy travel mode in an increasing number of cities. By encouraging two or more passengers to share a taxi, taxi sharing can cut down travel costs, reduce traffic emissions, and relieve traffic congestion. To promote taxi sharing, this paper first reviews the existing taxi sharing practices in cities. The entire process of taxi sharing can be broken up into the following: matching, route choice, and pricing. The current situation is revealed, as well as prospects for future developments. Building on the review, new approaches are proposed to taxi sharing matching, route choice modelling, and pricing schemes to improve taxi sharing practices and broaden their application.

Sensor and machine learning technologies have improved the perception of traffic systems by providing detailed data about individual vehicle trajectories. Combining data from different types of sensors shows promise for comprehensive perception of global traffic, but it remains challenging. Stationary roadside units only gather sparse trajectories of passing vehicles, while crowd-sourced data records entire trajectories but only consists of a very low sample rate of vehicles. Therefore, there is a need to learn route choice behavior from crowd-sourced data to infer complete paths for the sparse trajectories. Existing route choice models assume path set enumeration or the Markovian property for simplicity, which leaves room for capturing the long sequence of choice behavior from data for added precision. Additionally, the path inference problem is often broken down into multiple independent route choice problems between any consecutive sparse observations, leaving room for exploring one-shot long-sequence inference. To address these challenges, we propose RoutesFormer, an efficient sequence-based, data-driven route choice Transformer that requires minimal assumptions due to the capacity of the model architecture. By being sequence-based, RoutesFormer unifies the route choice and path inference problems, accommodating all observations together and avoiding the need to break down the problem into separate route choices, thereby improving optimality. Experiments conducted on the Shanghai taxi dataset demonstrate that RoutesFormer has made significant improvements over six existing baseline models in various challenging path inference tasks. Specifically, RoutesFormer has achieved state-of-the-art accuracy with an average total link length accuracy of 0.914/0.870 compared to the baselines' best average accuracy of 0.896/0.845, and it ranks first across all tasks. Additionally, the attention mechanism used in RoutesFormer is interpreted, providing a lens to study traveler's route choice behavior in the real world. • Innovates a sequence-based path inference model to remedy long-standing drawbacks. • Unifies path inference/route choice in an end-to-end paradigm. • Tailors two types of attention mechanisms to express complex route choice behavior. • Achieves SOTA accuracies, compared with six baselines from classic to deep models. [ABSTRACT FROM AUTHOR]

Traffic flow estimation (TFE) plays an important role in transportation systems, especially in relation to the detection, planning, and management of traffic for expressway systems. However, traditional approaches to TFE using roadside detectors usually have lowcoverage and high-cost problems. This paper proposes a toll ticket data (TTD)-based approach to TFE for filling the gaps in existing approaches and also considers multitype vehicles' impact on traffic flow for more accurate TFE results. First, route choice models are constructed to capture travel characteristics of mult-type vehicles and acquire route traffic flow from TTD. Then, a simulation-based dynamic traffic assignment (DTA) algorithm is developed to obtain traffic flow regarding vehicle type, time, and location, with the consideration of multitype vehicles' impact by using a tollbooth conversion factor (TCF) and point-of-queue model (PQM). The proposed approach is evaluated empirically using data relating to the Shandong Expressway Toll System in China. The results confirm that it performs effectively. This research could contribute to future TFE and provide references for dynamic, delicate traffic management in an economical and reliable way. [ABSTRACT FROM AUTHOR]

Recent studies adopted models of user acceptance of information technology to predict and explain drivers' acceptance of traffic information. Among these frameworks, the most commonly used is the Technology Acceptance Model (TAM). However, TAM is too general and does not consider drivers' response in specific traffic conditions or choice scenarios. This study combines an extended TAM with different choice scenarios displayed by Variable Message Signs (VMS) into a Hybrid Choice Model (HCM). Two models are proposed. The first model takes into account the causal relationships among latent variables based on the following hypotheses: Information Quality (IQ) has a positive effect on Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) which, in turn, have a positive effect on the Behavioural Intention (BI) to use traffic information. In the second model, the four latent variables PU, PEOU, IQ, and BI are directly added to the utility function without any causal relationships. 339 drivers with valid licence were interviewed via Stated Preference (SP) survey and the results show that TAM can explain travellers' response to VMS if the causal relationships among latent variables are taken into account. In addition, all hypothesized relationships are strongly supported. Practical and academic implications are also discussed. [ABSTRACT FROM AUTHOR]

This paper introduces a model to analyze route choice behavior of taxi drivers for finding next passenger in urban road network. Considering the situation of path overlapping between selected routes in the process of customer-searching, a mixed path size logit (MPSL) model is proposed to analyze route choice behaviors through considering spatio-temporal features of route including customer generation rate, path travel time, cumulative intersection delay, path distance, and path size. Specially, customer generation rate is defined as attraction strength based on historical pick-up records in the route, the intersection travel delay and path travel time are estimated based on large scaled taxi global positioning system (GPS) trajectories. In the experiment, the GPS data were collected from about 36000 taxi vehicles in Beijing at 30-s interval during six months. In the model application, an area of approximately 10 square kilometers in the center of Beijing is selected to demonstrate the effectiveness of the proposed model. The results indicated that the MPSL model could effectively analyze the route choice behavior in customer-searching process and express higher accuracy than traditional multinomial logit model and basic PSL model. [ABSTRACT FROM AUTHOR]

Exploring route choice in the context of tolled alternatives can support road operators to achieve better utilization of the infrastructure, as well as maximizing revenue collection. The research presented in this paper is conducted in the context of OPTIMUM, a European Union-funded project. The research objectives include a two-component system of models that proactively calculates commercial vehicles' toll prices. The component presented in this paper rests on the development of a route choice model that estimates the probabilities of using two alternative routes (toll road vs. national road), based on route attributes and user characteristics. To explore the usefulness of the proposed methodology a case study involving 50 truck drivers and 25 freight operators was conducted in Portugal between January 2016 and November 2017. Results from the route choice model reveal interesting insights about the role of incentives in the choice of toll roads, the perspectives of the different decision-makers and produce Values of Time for the study area. [ABSTRACT FROM AUTHOR]

• Analyzed truck route data from a freight survey in the Tokyo Metropolitan Area. • Developed a route choice model with the concept of maximizing overlapping ratio. • Predicted travel pattern changes in freight traffic for some policy scenarios. • Full operation of the three ring roads would smooth urban freight traffic. • Extension of weight-designated road might increase flow into residential areas. This study aims at predicting travel pattern changes of freight trucks in the Tokyo Metropolitan Area by taking full advantage of the latest large-scale urban freight survey. We found that travel patterns changed significantly depending on the type of trucks. A route choice model with the concept of the maximum route-overlapping ratio was developed and the calibrated model was found to be appropriate for our analysis. Finally, we analyzed various policy scenarios and found that the full operation of the three ring roads would contribute to smoother traffic whereas the extension of weight-designated road might have a relatively small impact. [ABSTRACT FROM AUTHOR]

When multiple routes connect the same origin and destination (OD), passenger route choices are related to the operation and management of urban rail transit, including the design of the train plan, fare clearing, and passenger distribution. The passengers' travel data from an automatic fare collection (AFC) system provides useful data for the analysis of passenger route choices. We propose an estimation method of passenger route choices based on AFC data, which includes passenger's travel time and OD. In this paper, the AFC data used for analysis were mined to eliminate unreasonable individual travel data. In this study, the components of passenger travel times and their influencing factors are analyzed and the results indicate that the travel times are related to the route choices of passengers. Cluster analysis indicated that passenger travel times follow the logarithmic normal distribution. Based on this and a multi-route distribution sample, an estimation model for passenger route choice proportions is proposed. Furthermore, experimental results show that the data mining method has high accuracy and the result also supported the hypothesis about the travel time distribution. The model was applied to the Chengdu metro, which is one of the largest metro systems in the world, and the obtained results indicate that the model works well. In addition to the initial application, the estimation method provides a new method to calculate the route choice proportion for urban rail transit with maximum dependability and timeliness. [ABSTRACT FROM AUTHOR]

The traffic behaviours of commuters may cause traffic congestion during peak hours. Advanced Traffic Information System can provide dynamic information to travellers. Due to the lack of timeliness and comprehensiveness, the provided information cannot satisfy the travellers’ needs. Since the assumptions of traditional route choice model based on Expected Utility Theory conflict with the actual situation, a route choice model based on Game Theory is proposed to provide reliable route choice to commuters in actual situation in this paper. The proposed model treats the alternative routes as game players and utilizes the precision of predicted information and familiarity of traffic condition to build a game. The optimal route can be generated considering Nash Equilibrium by solving the route choice game. Simulations and experimental analysis show that the proposed model can describe the commuters’ routine route choice decision exactly and the provided route is reliable.

Passive monitoring with the Global Positioning System (GPS) is increasingly used to automatically monitor trip data. However, GPS tracking data includes measurement errors that depend on the monitoring device and network description in the model. In the case of urban pedestrian networks, such as city centers, the built environment of streets is often diverse, and this has a significant impact on the measurement. The errors cause the biased observations of route choices, and thus the parameter estimation results of route choice models are also biased. To deal with this problem of biased estimation, this study proposes a link-based route measurement model that sequentially infers links using decomposed sequences of data and estimates the link-specific variance of the GPS measurement error. We also incorporate a link-based route choice model as the prior to correct the measurement model by considering behavioral mechanism without path enumeration. Additionally, to remove the biases included in the prior information, this study proposes a structural estimation method in which the fixed point problem of behavioral parameter is solved by the iteration process. The performance of the proposed methods is examined both through a numerical example and a case study on a real pedestrian network. As the results, the methods refine the performance of the route measurement model, and the estimated parameters of a route choice model obtained by the structural estimation method are less biased and exhibit a different trend than those using the biased route choice observations. Also, the estimated variances of the GPS measurement errors are realistic. [ABSTRACT FROM AUTHOR]

Emerging sensing technologies such as probe vehicles equipped with Global Positioning System (GPS) devices on board provide us real-time vehicle trajectories. They are helpful for the understanding of the cases that are significant but difficult to observe because of the infrequency, such as gridlock networks. On the premise of this type of emerging technology, this paper propose a sequential route choice model that describes route choice behavior, both in ordinary networks, where drivers acquire spatial knowledge of networks through their experiences, and in extraordinary networks, which are situations that drivers rarely experience, and applicable to real-time traffic simulations. In extraordinary networks, drivers do not have any experience or appropriate information. In such a context, drivers have little spatial knowledge of networks and choose routes based on dynamic decision making, which is sequential and somewhat forward-looking. In order to model these decision-making dynamics, we propose a discounted recursive logit model, which is a sequential route choice model with the discount factor of expected future utility. Through illustrative examples, we show that the discount factor reflects drivers’ decision-making dynamics, and myopic decisions can confound the network congestion level. We also estimate the parameters of the proposed model using a probe taxis’ trajectory data collected on March 4, 2011 and on March 11, 2011, when the Great East Japan Earthquake occurred in the Tokyo Metropolitan area. The results show that the discount factor has a lower value in gridlock networks than in ordinary networks. [ABSTRACT FROM AUTHOR]

In this paper we propose how available dataset can be used to estimate rerouting phenomena in traffic networks. We show how to look at set of paths observed during unexpected events to understand the rerouting phenomena. We use the information comply model [1] and propose its estimation method. We propose the likelihood formula and show how the theoretical and observed rerouting probabilities can be obtained. We conclude with illustrative example showing how a single observed path can be processes and what information it provides. Contrary to parallel paper [2] where rerouting phenomena is estimated using real traffic flow measures from Warsaw, here we use only synthetic data. The paper is organized as follows. First we elaborate on rerouting phenomena and define the traffic network, then we summarize the literature behind rerouting phenomena. We follow with a synthetic definition of dynamic traffic assignment needed to introduce ICM model in subsequent section. Based on that introduction we define the observations and propose estimation method based on them followed by illustrative example. Paper is summarized with conclusions and pointing of future directions.

This research describes numerical methods to analyze the absolute transport demand of cyclists and to quantify the road network weaknesses of a city with the aim to identify infrastructure improvements in favor of cyclists. The methods are based on a combination of bicycle counts and map-matched GPS traces. The methods are demonstrated with data from the city of Bologna, Italy: approximately 27,500 GPS traces from cyclists were recorded over a period of one month on a volunteer basis using a smartphone application. One method estimates absolute, city-wide bicycle flows by scaling map-matched bicycle flows of the entire network to manual and instrumental bicycle counts at the main bikeways of the city. As there is a fairly high correlation between the two sources of flow data, the absolute bike-flows of the entire network have been correctly estimated. Another method describes a novel, total deviation metric per link which quantifies for each network edge the total deviation generated for cyclists in terms of extra distances traveled with respect to the shortest possible route. The deviations are accepted by cyclists either to avoid unpleasant road attributes along the shortest route or to experience more favorable road attributes along the chosen route. The total deviation metric indicates to the planner which road links are contributing most to the total deviation of all cyclists. In this way, repellant and attractive road attributes for cyclists can be identified. This is why the total deviation metric is of practical help to prioritize bike infrastructure construction on individual road network links. Finally, the map-matched traces allow the calibration of a discrete choice model between two route alternatives, considering distance, share of exclusive bikeway, and share of low-priority roads.

With the growing use of crowdsourced location data from smartphones for transportation applications, the task of map-matching raw location sequence data to travel paths in the road network becomes more important. High-frequency sampling of smartphone locations using accurate but power-hungry positioning technologies is not practically feasible as it consumes an undue amount of the smartphone’s bandwidth and battery power. Hence, there exists a need to develop robust algorithms for map-matching inaccurate and sparse location data in an accurate and timely manner. This paper addresses the above-mentioned need by presenting a novel map-matching solution that combines the widely used approach based on a hidden Markov model (HMM) with the concept of drivers’ route choice. Our algorithm uses an HMM tailored for noisy and sparse data to generate partial map-matched paths in an online manner. We use a route choice model, estimated from real drive data, to reassess each HMM-generated partial path along with a set of feasible alternative paths. We evaluated the proposed algorithm with real world as well as synthetic location data under varying levels of measurement noise and temporal sparsity. The results show that the map-matching accuracy of our algorithm is significantly higher than that of the state of the art, especially at high levels of noise. [ABSTRACT FROM PUBLISHER]

This paper proposes a route choice model that incorporates the various behavioral mechanisms in route choice proposed in the literature including the shortest path, bounded rationality, asymmetric preference, and the time surplus maximization. In the proposed model, travelers are assumed to compare travel cost to their status quo (travel cost of the currently used path) in deciding whether to switch to another alternative, and the underlying value of time is adaptive in the sense that it varies across different route choice contexts. We find that the status quo-dependent route choice model can handle the route choice inertia resulting from different sources (e.g., travelers' misperceptions, satisficing behavior, asymmetric preference). Moreover, the inertia is path-specific and can incorporate the scaling effect of travel cost on travelers' route choices. Examples are provided to illustrate the proposed status quo-dependent route choice model as well as its connection with various existing route choice models. [ABSTRACT FROM AUTHOR]

We consider a discrete intermodal network design problem for freight transportation, in which the network planner needs to determine whether or not to build up or expand a link to minimize the total operating cost of carriers and hub operators under a general route choice model of intermodal operators. We formulate the problem as a mixed-integer nonlinear and non-convex program that involves congestion effects, piecewise linear cost functions, and a fixed-point constraint. We develop a series of relaxed and equivalent models to reduce the hardness of the problem and provide theoretical results to show the equivalences. We present two solution methods to solve the problem with one returning heuristic solutions and the other generating a globally optimal solution. We offer two numerical experiments to test the two solution algorithms and also shed light on their performance comparisons. [ABSTRACT FROM AUTHOR]

The heterogeneity of passengers’ route choice has been explained by randomizing the parameters, also known as taste parameters , that determine the way the attributes are relatively weighed in the disutility he/she perceives from a route. Growing availability of massive route choice data from, e.g. GPS or Smart Card system has made expected a model that derives the distribution of taste parameters from RP-data rather than relies on a prescribed distribution. This study availed itself of the intensive set of route choice data from Smart Card system as well as inverse optimization to calibrate the joint pdf of taste parameters to best signify the user-optimality of observed routes. Tested on 5 daily sets of real route choice, which amounts to 50,000 trips from the metro of Seoul metropolitan area, the proposed model notably enhanced the predictability compared to the previous models adopting a mixed-logit-based SUE or a non-parametric estimation method. [ABSTRACT FROM AUTHOR]

Traffic equilibrium models are fundamental to the analysis of transportation systems. The stochastic user equilibrium (SUE) model which relaxes the perfect information assumption of the deterministic user equilibrium is one such model. The aim of this paper is to develop a new user equilibrium model, namely the MDM-SUE model, that uses the marginal distribution model (MDM) as the underlying route choice model. In this choice model, the marginal distributions of the path utilities are specified but the joint distribution is not. By focusing on the joint distribution that maximizes expected utility, we show that MDM-SUE exists and is unique under mild assumptions on the marginal distributions. We develop a convex optimization formulation for the MDM-SUE. For specific choices of marginal distributions, the MDM-SUE model recreates the optimization formulation of logit SUE and weibit SUE. Moreover, the model is flexible since it can capture perception variance scaling at the route level and allows for modeling different user preferences by allowing for skewed distributions and heavy tailed distributions. The model can also be generalized to incorporate bounded support distributions and discrete distributions which allows to distinguish between used and unused routes within the SUE framework. We adapt the method of successive averages to develop an efficient approach to compute MDM-SUE traffic flows. In our numerical experiments, we test the ability of MDM-SUE to relax the assumption that the error terms are independently and identically distributed random variables as in the logit models and study the additional modeling flexibility that MDM-SUE provides on small-sized networks as well as on the large network of the city of Winnipeg. The results indicate that the model provides both modeling flexibility and computational tractability in traffic equilibrium. [ABSTRACT FROM AUTHOR]

Finding a scenic route between two locations is a common trip planning task, in particular for tourists and recreational travelers. For the automated computation of a scenic route in a trip planning system it is necessary to identify which attributes of a route and its surroundings are associated with attractive scenery. This study uses a route choice model, more specifically a Path Size Logit (PSL) model, to identify the relevant attributes and their relative importance. Three hypotheses are formulated and tested with three PSL models to understand the effects of different attributes on scenic route selection. The set of chosen scenic routes are based on various VGI (Volunteered Geographic Information) data that have been extracted for California as a study region. The results identify several variables of the surrounding environment as significant contributors to route scenery after controlling for road type. [ABSTRACT FROM AUTHOR]