Route Choice Model Research Articles

Proposing a Simulation-Based Dynamic System Optimal Traffic Assignment Algorithm for SUMO: An Approximation of Marginal Travel Time

User equilibrium (UE) and system optimal (SO) are among the essential principles for solving the traffic assignment problem. Many studies have been performed on solving the UE and SO traffic assignment problem; however, the majority of them are either static (which can lead to inaccurate predictions due to long aggregation intervals) or analytical (which is computationally expensive for large-scale networks). Besides, most of the well-known micro/meso traffic simulators, do not provide a SO solution of the traffic assignment problem. To this end, this study proposes a new simulation-based dynamic system optimal (SB-DSO) traffic assignment algorithm for the SUMO simulator, which can be applied on large-scale networks. A new swapping/convergence algorithm, which is based on the logit route choice model, is presented in this study. This swapping algorithm is compared with the Method of Successive Average (MSA) which is very common in the literature. Also, a surrogate model of marginal travel time was implemented in the proposed algorithm, which was tested on real and abstract road networks (both on micro and meso scales). The results indicate that the proposed swapping algorithm has better performance than the classical swapping algorithms (e.g. MSA). Furthermore, a comparison was made between the proposed SB-DSO and the current simulation-based dynamic user equilibrium (SB-DUE) traffic assignment algorithm in SUMO. This proposed algorithm helps researchers to better understand the impacts of vehicles that may follow SO routines in future (e.g., Connected and Autonomous Vehicles (CAVs)).

Bi-level clustering of vehicle trajectories for path choice set and its nested structure identification

Path choice set identification is essential for route choice modelling and travel behaviour studies. Recent advancements in data collection techniques have gained attention towards a data-driven choice set identification process. However, empirical vehicle trajectory datasets result in several path observations compared to the traditional algorithms, complicating the route choice modelling process. This study proposes a bi-level vehicle trajectory clustering framework where the output of the upper-level clustering provides a representative path choice set for simple/mixed logit modelling (MNL), whereas the lower-level clustering provides a nested or cross-nested representation of the paths based on hard and soft clustering, respectively.As proof of concept, the proposed methodology is applied on real Bluetooth-based trajectories from Brisbane, where 62 unique paths were observed from a one-year trajectory data for an origin–destination pair. The results of the MNL model for the representative paths provide desirable magnitude with negative coefficients for the distance and travel time path attributes. Further, the results of the (cross) nested modelling appropriately identified the (cross) nested structure for the path choice set.

Modeling and analyzing the traffic flow during evacuation in Hurricane Irma (2017)

Hurricane evacuation modeling is challenging due to a scarcity of evacuation data and the complexity of human decision-making and travel behavior. We build a system for rapidly predicting the hurricane evacuation traffic flow based on hurricane forecasting, evacuation orders, the road network, and population information. The system integrates an evacuation demand model, an origin–destination model, and a route choice model into a link flow-based mean-field traffic model. We evaluate and calibrate the model with traffic observations from Hurricane Irma (2017), which induced a massive evacuation and traffic congestions throughout Florida State. The model skillfully captures the spatial and temporal evacuation features, including peak traffic flows and daily traffic fluctuations. The model can be applied to support evacuation management. Our analysis shows that a minor adjustment to the evacuation order could considerably alleviate the traffic congestion during Hurricane Irma.

Online Route Choice Modeling for Mobility-as-a-Service Networks With Non-Separable, Congestible Link Capacity Effects

With the prevalence of MaaS systems, route choice models need to consider characteristics unique to them. MaaS systems tend to involve service systems with fleets of vehicles; as a result, the available service capacity depends on the choices of other travelers in different parts of the system. We model this with a new concept of “congestible capacity”; that is, link capacities are a function of flow instead of link costs. This dependency is also non-separable; the capacity in one link can depend on flows from multiple links. An offline-online estimation method is introduced to capture the structural effects that flows have on capacities and the resulting impacts on route choice utilities. The method is first applied to obtain unique congestible capacity shadow prices in a multimodal network to verify the capability to capture congestion effects on capacities. The capacities are shown to vary and impact the utility of a route. The method is validated using real system data from Citi Bike in New York City. The results show that the model can fit to the data quite well and performs better than a baseline modeling approach that ignores congestible capacity effects. By relating the route choice to congestible capacities using a random utility model, modelers can monitor and quantify the impacts to traveler consumer surplus in real time. Applications of the model and online method include monitoring capacity effects on consumer surplus, using the model to direct incentives programs for rebalancing and other revenue management strategies, and to guide resource allocation to mitigate consumer surplus impacts due to disruptions from incidents.

Transfer Penalty Estimation of Bus Passengers Considering Travel Frequency in Chengdu, China

The paper aims to examine bus passengers’ route choice behavior in the context of transfer penalty estimation. Methodologically, it leverages passengers’ stop-level trip information inferred from automatic vehicle location (AVL) and automated fare collection (AFC) data and compares the stop-level origin–destination (O-D) pairs with direct paths and transfer paths. The route choice models were established with the inclusion of normalized travel frequency as a key variable. A case study was performed using the trip data of adult card holders from the bus systems in Chengdu, China. The results show that the normalized travel frequency is a variable that makes an informative contribution to the explanation of passenger route choices. Specifically, passengers with higher travel frequency prefer to choose the transfer path. Moreover, the transfer penalty function in the context of the monthly travel frequency was obtained and shows that the travel frequency can reduce a passenger’s transfer penalty. Finally, the stabilities of the normalized travel frequency function and transfer penalty function were assessed using the trip data from the weekdays in four weeks with the results showing that the normalized travel frequency function is similar in shape. In addition, the relationship between a passenger’s monthly travel frequency and their familiarity with the bus network was discussed using the second derivative of the normalized travel frequency function.

Route choice estimation in rail transit systems using smart card data: handling vehicle schedule and walking time uncertainties

Several cities around the world rely on urban rail transit systems composed of interconnected lines, serving massive numbers of passengers on a daily basis. Accessing the location of passengers is essential to ensure the efficient and safe operation and planning of these systems. However, passenger route choices between origin and destination pairs are variable, depending on the subjective perception of travel and waiting times, required transfers, convenience factors, and on-site vehicle arrivals. This work proposes a robust methodology to estimate passenger route choices based only on automated fare collection data, i.e. without privacy-invasive sensors and monitoring devices. Unlike previous approaches, our method does not require precise train timetable information or prior route choice models, and is robust to unforeseen operational events like malfunctions and delays. Train arrival times are inferred from passenger volume spikes at the exit gates, and the likelihood of eligible routes per passenger estimated based on the alignment between vehicle location and the passenger timings of entrance and exit. Applying this approach to automated fare collection data in Lisbon, we find that while in most cases passengers preferred the route with the least transfers, there were a significant number of cases where the shorter distance was preferred. Our findings are valuable for decision support among rail operators in various aspects such as passenger traffic bottleneck resolution, train allocation and scheduling, and placement of services.

A dynamic continuum route choice model for pedestrian flow with mixed crowds

ABSTRACT This study develops a dynamic route choice model for pedestrian with mixed crowds. Pedestrian flow is regarded as a two-dimensional compressible continuum fluid. Then, characteristic variables are described with mathematical functions. Pedestrians are classified into two classes based on different route choice strategies: reactive and predictive dynamic user-optimal principles. Reactive pedestrians only consider the current information to choose the routes with the minimum instantaneous cost. Predictive pedestrians are assumed to know details about the future and choose routes with the minimum predictive actual travel cost. Two methods are used to solve the models. Numerical simulations are presented to demonstrate the effectiveness of the models and the algorithms. The numerical results show that the evacuation time of predictive pedestrians is shorter than that of reactive pedestrians. Moreover, for low-density situations, predictive pedestrians can help improve the overall evacuation efficiency. However, for high-density situations, predictive pedestrians fail to improve the efficiency.

Shared Clean Mobility Operations for First-Mile and Last-Mile Public Transit Connections: A Case Study of Doha, Qatar

With the aid of recent technological advancements, seamless integration of shared mobility services and public transit may offer efficient and affordable connectivity to the transit stations in urban settings, thereby enhancing residents’ mobility. A previous research mainly focused on car-sharing services as a self-standing mode of transportation. However, due to rapid urbanization acceleration and regions’ extension, commuters often combine the fixed-route/fixed schedules public transportation and car-sharing service in one journey. To this end, we study a one-way, station-based electric car-sharing service interaction with public transportation. We propose an integrated route choice and EV assignment model to address the potential of car-sharing services as a feeder to the public transit network. The integrated model consists of two components, operations of the car-sharing service and the commuter’s route choice and the associated mode choice. The service provider decides on the resource levels, allocations, and relocation strategy in the first component. In the second component, the travel options for the commuters are modeled. The two-component model was simulated in an agent-based simulation based on a case study from the state of Qatar. We further extend the integrated model to include the carpooling option, in which multiple passengers sharing the same route can share the same vehicle. Extensive simulation analyses show that the integration can considerably enhance urban mobility and increase public transportation accessibility through enhanced first and last miles linkages. Moreover, the influence of transportation supply and spatial characteristics on the individual mode choice was estimated. Results indicate that public transit ridership can increase up to 17%. Moreover, adding the carpooling option can significantly decrease the number of relocations operations at a minimal impact on the commuters’ trip performance.

Estimating Route Choice Characteristics of Truck Drivers from Sparse Automated Vehicle Identification Data through Data Fusion and Bi-Objective Optimization

Optimizing route choices for truck drivers is a key element in achieving reliable road freight operations. For commercial reasons, it is often difficult to collect freight activity data through traditional surveys. Automated vehicle identification (AVI) data on fixed locations (e.g., Bluetooth or camera) are low-cost alternatives that may have the potential to estimate route choice models. However, in cases where these AVI sensors are sparsely located, the resulting data lack actual route choices (or labels), which limits their application estimating route choice models. This paper overcomes this limitation with a new two-step approach based on fusing AVI and loop-detector data. First, a sparse Bluetooth data set is fused with travel times estimated from densely spaced loop-detector data. Second, the combined data set is fed into a bi-objective optimization method which simultaneously infers the actual route choices of truck drivers between an origin–destination pair and estimates the parameters of a route choice (discrete choice-based) model. We apply this approach to investigate the route choice behavior of truck drivers operating to and from the port of Rotterdam in the Netherlands. The proposed model can distinguish between peak and off-peak periods and identify different segments of truck drivers based on a latent classes choice analysis. Our results indicate the potential of traffic and logistics interventions in improving the route choices of truck drivers during peak hours. Overall, this paper demonstrates that it might be possible to estimate route choice characteristics from readily available data that can be retrieved from traffic management agencies.

Effect of the Street Environment on Walking Behavior: A Case Study Using the Route Choice Model in the Chunliu Community of Dalian.

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.

Estimation of Route Choice Behaviors of Bike-Sharing Users as First- and Last-mile Trips for Introduction of Mobility-as-a-Service (MaaS)

Bicycling has been considered the sustainable alternatives of first- and last-mile trips for Mobility-as-a-Service (MaaS) in integrated transport systems. This study aims to analyze the route choice behaviors of bike-sharing users for the first- and last-mile trips in MaaS, incorporating the connectivity to the subway stations. The observed GPS data is employed to generate the route travels of bike-sharing users, and the choice set generation algorithm, i.e., the k-medoids clustering method, is applied to extract the representative alternatives in route choice models. This study analyzes the path-size logit model to compare the route choice behaviors incorporating peak periods, and access and egress trips. The bike-sharing use at the evening-peak period is more frequent than the other periods. Also, the bike-sharing users are found to be positively associated with the ratio of bike lanes for the access and egress trips to the subway stations, whereas they are reluctant to detour for their trips. The finding of this research is that the extension of bike facilities on access and egress to the subway stations is necessary to improve the bike-sharing use for the first- and last-mile trips in MaaS.

Choice set robustness and internal consistency in correlation-based logit stochastic user equilibrium models

The Stochastic User Equilibrium (SUE) traffic assignment model is a well-known approach for investigating the behaviours of travellers on congested road networks. SUE compensates for driver/modelling uncertainty of the route travel costs by supposing the costs include stochastic terms. Two key challenges for SUE modelling, however, are capturing route correlations and dealing with unrealistic routes. Numerous correlation-based SUE models have been proposed, but issues remain over both internal consistency and choice set robustness. This paper formulates (and proves solution existence for) new internally consistent SUE formulations of GEV structure and correction term logit route choice models, where the functional forms in the correlation components are based upon generalised, flow-dependent congested costs, rather than e.g. length / free-flow travel time as done typically. Numerical experiments are then conducted on the Sioux Falls and Winnipeg networks, where computational feasibility for obtaining internally consistent solutions, choice set robustness, and internal consistency are assessed/compared.

Solving a Multi-Class Traffic Assignment Model with Mixed Modes

In comparison to conventional human-driven vehicles (HVs), connected and automated vehicles (CAVs) provide benefits (e.g., reducing travel time and improving safety). However, before the period of fully CAVs appears, there will be a situation in which both HVs and CAVs are present, and the traffic flow pattern may differ from that of a single class (e.g., HV or CAV). In this study, we developed a multi-class traffic assignment problem (TAP) for a transportation network that explicitly considered mixed modes (e.g., HV and CAV). As a link’s travel time is dependent on the degree of mixed flows, each mode required an asymmetric interaction cost function. For TAP, the multi-class user equilibrium (UE) model was used for the route choice model. A route-based variational inequality (VI) formulation was used to represent the multi-class TAP and solve it using the gradient projection (GP) algorithm. It has been demonstrated that the GP algorithm is an effective route-based solution for solving the single-class user equilibrium (UE) problem. However, it has rarely been applied to solving asymmetric UE problems. In this study, the single-class GP algorithm was extended to solve the multi-class TAP. The numerical results indicated the model’s efficacy in capturing the features of the proposed TAP utilizing a set of simple networks and real transportation networks. Additionally, it demonstrated the computational effectiveness of the GP algorithm in solving the multi-class TAP.

The Cheapest Ticket Problem in Public Transport

Route choice models in public transport have been discussed for a long time. The main reason why a passenger chooses a specific path is usually based on its length or travel time. However, also the ticket price that passengers have to pay may influence their decision because passengers prefer cheaper paths over more expensive ones. In this paper, we deal with the cheapest ticket problem, which asks for a cheapest ticket to travel between a pair of stations. The complexity and the algorithmic approach to solve this problem depend crucially on the underlying fare structure; for example, it is easy if the ticket price is proportional to the distance traveled (as in distance tariff fare structures), but may become NP-complete in zone tariff fare structures. We hence discuss the cheapest ticket problem for different variations of distance- and zone-based fare structures. We start by modeling the respective fare structure mathematically, then identify its main properties, and finally provide a polynomial algorithm, or prove NP-completeness of the cheapest ticket problem. We also provide general results on the combination of two fare structures, which is often observed in practice.

Implicit GPS-based bicycle route choice model using clustering methods and a LSTM network.

Biking is gaining in popularity all around the world as a healthy and environmentally friendly mode of transportation. Urban policies tend to encourage citizens to use bicycles. This can be done by creating new cycling infrastructures, the renovation of old ones or the deployment of bike-sharing systems (BSS). These policies having a cost, understanding and predicting the behavior of cyclists has become a necessity in order to optimize them. Classical methods analyzing cyclists' route choices use external factors and generated choice sets of paths along with a logit model to create a discrete route choice model. Nevertheless, few studies focus on the predictive capacity that this type of model can offer. In this paper, we developed a prediction-centered bicycle route choice model. Our model is created without using external factors or choice sets of paths as in the more classical methods. The idea of our method is to use deep and machine learning algorithms on GPS tracks. These algorithms learn representations from the data which replace explicit factors. To build the model, we clustered the GPS tracks using DBSCAN. The clusters allow to identify the cyclists' preferred road segments and are used to create paths using them. A method weighting the road graph weights is developed to create paths passing through the preferred road segments of a given cluster. A LSTM is finally trained in order to retrieve a cluster from a shortest path between an origin/destination pair. Tracks created by our model are more similar to the original GPS tracks than the shortest paths or tracks generated by a prominent path computation service.

A Route Choice Model for Road Network Users in Mountainous Cities considering Vulnerability

Urban resilience has become one of the core arguments of sustainable urban development. The urban road system should also respond effectively to various changes or impacts and reduce the uncertainty and vulnerability of road network caused by emergencies. A route choice model considering the vulnerability was proposed. The model analyzed the traveler’ behavior in complex road network. The model is validated by the road network in Chongqing city. First, the vulnerability of road network in mountainous cities is defined, and the failure situation and state classification of road network considering vulnerability are analyzed. The results show that travelers with different sensitive states have obvious different route choice behavior in vulnerable networks. Since nonsensitive users do not reserve travel time in the early days, they are more inclined to respond to emergencies quickly and then choose fast detour paths. However, sensitive users have set off earlier than normal time and have psychological expectations of emergencies. They have more time to maintain the existing path. Finally, a calculation example is used to test that a certain section of the road network fails randomly, and network users can effectively choose the individual optimal route under different failure states.

Nonparametric Regression Analysis of Cyclist Waiting Times across Three Behavioral Typologies

This paper seeks to predict the average waiting time, defined as the time spent moving at 1 ms−1 or less, of urban bicyclists during rush hours while performing different maneuvers at intersections. Individual predictive models are built for the three cyclist typologies previously identified on a large database of GPS traces recorded in the city of Bologna, Italy. Individual models are built for the three cyclist typologies and bootstrapping has confirmed the validity and robustness of the results. The results allow the integration of waiting times in route choice models for cyclists, thus improving the rational bases by which cyclists makes their decisions. Moreover, the modeling allows transportation engineers to understand how different cyclist typologies perceive different variables that affect their waiting times. Future work should focus on testing the model transferability to other case studies.

The taxi sharing practices: Matching, routing and pricing methods

• Conduct a comprehensive review of the existing taxi sharing practices in cities. • Examine the key issues in taxi sharing: matching methods, route choice optimization models, and pricing schemes. • Establish a matching and routing joint optimization model. • Propose a fare sharing method that caters to complex combinations of passengers. 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.

Sensitivity analysis for transit equilibrium assignment and applications to uncertainty analysis

Systematic uncertainty analysis can be used to quantitatively evaluate variation in model outputs and identify the critical sources of uncertainty to improve the reliability and stability of a system. To analyze the effects of uncertainties in transit networks that may be caused by probabilistic travel demand, congestion, or vehicle frequencies, this study develops a sensitivity-based uncertainty analysis approach as a post-analysis tool for equilibrium transit systems. The congestion effect is considered in the waiting time and in-vehicle travel time of a passengers’ route-choice model. The hyperpath concept is used to manage passengers’ riding strategies due to the common-line problem at transit stops. A hyperpath-based gradient projection (GP) solution algorithm is developed for the solution of the variational inequality formulation of the transit equilibrium assignment problem (TEAP). A restricted sensitivity analysis approach originally developed for road networks is re-developed for the TEAP in transit networks. An analytical sensitivity-based approach is derived to conduct uncertainty analysis for the TEAP, which enables the simultaneous propagation of uncertainties from different input sources to the model outputs. Numerical examples are provided for the following purposes. (1) To demonstrate three applications of the sensitivity analysis of the TEAP, namely the perturbed solution estimation problem, the critical parameter identification problem, and the paradox analysis problem. (2) To illustrate the use of uncertainty analysis of the TEAP, such as estimating the variance and confidence level of model outputs with respect to various model inputs/parameters as random variables, and ranking the importance of arcs using sensitivity-based uncertainty analysis. (3) To demonstrate the applicability of the proposed approach to real transit networks. The findings demonstrate not only the importance of the analytical sensitivity analysis development for the TEAP, but also for the practical applications of sensitivity and uncertainty analyses.

Justifying toll payment with biased travel time estimates: Behavioral findings and route choice modeling

Justifying toll payment with biased travel time estimates: Behavioral findings and route choice modeling