User Route Choice Research Articles

E-scooter route choice in Germany – using stated preference data to investigate e-scooter route choice preferences

Electric kick scooter (e-scooter) have to use bicycle infrastructure in Germany. However, the illegal use of space exclusively dedicated to other modes (e.g. walking) is frequently observed. In order to understand why e-scooter users do (not) use the dedicated infrastructure it is essential to understand e-scooter route choice. The article presents results of an online survey and a stated choice experiment conducted to analyze route choice of e-scooter users in Germany. Data collection from 2020 finally resulted in 3,246 stated route decisions from 6,492 route alternatives responded by 424 participants. The analysis revealed that increasing travel time, number of intersections and high traffic volumes of motorized traffic along the route decrease route choice probability. In contrast, increasing the share of fine surface and bicycle infrastructure lead to an increase of route choice probability.

Map-Matching on Low Sampling Rate Trajectories through Frequent Pattern Mining

Map-matching, an important preprocessing task in many location-based services (LBS), projects each point of the global positioning system (GPS) within a trajectory dataset onto a digital map. The state-of-the-art map-matching algorithms typically employ Hidden Markov model (HMM) via shortest path computation. But the computation of the shortest path might not work well on low-sampling-rate trajectory data (e.g., one GPS point every 1–5 min), leading to low matching precision and high running time. To solve the problem, this paper firstly identifies frequent patterns (FPs) in historical trajectories to capture meaningful mobility behaviors, and then extracts mobile behavior criterion (MBC) of mobile users. Such a criterion generally represents the route choice of mobile users on road networks. Moreover, the temporal information within trajectory data was employed to estimate the speed of mobile users on road segments. The identified FPs, coupled with MBC and moving speed, help to improve the map-matching precision of low-sampling-rate trajectories. In addition, an FP-forest structure was proposed to index the identified FPs. The structure could greatly speed up the lookup of frequent paths for shorter running time. Furthermore, the FP-forest structure was pruned to reduce redundancy with smaller space cost. Finally, experiments were carried out on real-world datasets. The results confirm that our FP-matching method outperforms state-of-the-art in terms of effectiveness and efficiency.

Cordon Pricing Scheme Based on Macroscopic Fundamental Diagram and Distance

To alleviate traffic congestion in key areas, such as city centers, this study proposes an optimal pricing strategy that is based on the distance that users travel in a given cordon. The proposed strategy is derived from the theory of the macroscopic fundamental diagram (MFD). In this strategy, a non-negative, nondecreasing, and nonlinear distance-toll function is defined by a piecewise linear approximation method. A bilevel programming model is then constructed on the basis of the toll charge function and the characteristics of the MFD. The upper model considers the overall operating efficiency of the system and aims to maximize the outflow of the road network. The lower model assumes that user route choice behavior complies with Wardrop’s first principle to formulate a user equilibrium model under a fixed demand. In view of the nonadditive property of path cost imposed by the nonlinear distance-toll function, the network transformation technique is used to realize the conversion between real networks and virtual networks. The Frank-Wolfe algorithm based on road segments is then used to solve the lower model. The verification of the reasonable feasibility of the pricing model and algorithm reveals that the implementation of cordon pricing schemes based on distance can make the outflow of the border area maintain the best level. Moreover, the obtained optimal charge function makes the system run efficiently. These results prove that the nonlinear optimal toll charge function is realistic and effective.

Route choice of bike share users: Leveraging GPS data to derive choice sets

To identify the determinants of bike share users' route choices, this research collects 132,397 hub-to-hub global positioning system (GPS) trajectories over a 12-month period between April 1, 2015 and March 31, 2016 from 750 bicycles provided by Hamilton Bike Share (HBS). A GIS-based map-matching algorithm is used to derive users' routes along the cycling network within Hamilton, Ontario and generate multiple attributes for each route, such as route distance, route directness, average distance between intersections, and the number of turns, intersections, and unique road segments. Concerning route choice analysis, the origin and destination pair should be the same for all routes within a choice set, thus HBS users' trips are grouped by origin-destination hub pairs. Since trips taken by different users between a hub pair can follow the same route, unique routes are extracted using a link signature extraction tool. Following this, a normalized Gini (Gn) coefficient is calculated for each hub pair to evaluate users' preferences among all the unique hub-to-hub route choices. A Gn closer to 0 indicates that routes between a hub pair are more evenly used, while a value closer to 1 implies a higher preference toward one dominant route. Three route choice models, a global model, a medium Gn model, and a high Gn model, are estimated using Path-Size Logit to determine how route choice is affected by the presence of dominant routes. These models suggest that HBS users are willing to detour for some attributes, such as bicycle facilities, but tend to avoid circuitous routes, turns, steep slopes, and roads with high traffic volume.

A many-to-many assignment game and stable outcome algorithm to evaluate collaborative mobility-as-a-service platforms

As Mobility as a Service (MaaS) systems become increasingly popular, travel is changing from unimodal trips to personalized services offered by a platform of mobility operators. Evaluation of MaaS platforms depends on modeling both user route decisions as well as operator service and pricing decisions. We adopt a new paradigm for traffic assignment in a MaaS network of multiple operators using the concept of stable matching to allocate costs and determine prices offered by operators corresponding to user route choices and operator service choices without resorting to nonconvex bilevel programming formulations. Unlike our prior work, the proposed model allows travelers to make multimodal, multi-operator trips, resulting in stable cost allocations between competing network operators to provide MaaS for users. An algorithm is proposed to efficiently generate stability conditions for the stable outcome model. Extensive computational experiments demonstrate the use of the model to handling pricing responses of MaaS operators in technological and capacity changes, government acquisition, consolidation, and firm entry, using the classic Sioux Falls network. The proposed algorithm replicates the same stability conditions as explicit path enumeration while taking only 17 seconds compared to explicit path enumeration timing out over 2 hours.

What Makes a Good Cargo Bike Route? Perspectives from Users and Planners

AbstractCargo bikes—bicycles made to carry both goods and people—are becoming increasingly common as an alternative to automobiles in urban areas. With a wider and heavier body, cargo bikes often face problems even in the presence of cycling infrastructure, thus limiting their possibilities of route choice. Infrastructure quality and the route choices of cyclists have been well studied, but often solely based on a quantitative approach, leading to tools such as BLOS (bicycle level of service). With various designs of cargo bikes being used for a wide range of purposes, the route choice of cargo bike users is difficult to generalize. This study combines quantitative and qualitative approaches in order to explore what is important for cargo bike users’ route choice, and how this knowledge can be effectively used for planning. Our results suggest that while some general preferences exist, route choice involves complex dynamics that cannot be fully explained by quantitative measures alone: in addition to understanding “what” is important for cargo bike users, we need to understand “why” it is important. Furthermore, route choice is also influenced by the city context, making a study tailored to the local context essential.

Traffic Flow Control in Vehicular Multi-Hop Networks with Data Caching

Control of conventional transportation networks aims at bringing the state of the network (e.g., the traffic flows in the network) to the system optimal (SO) state. This optimum is characterized by the minimality of the social cost function, i.e., the total cost of travel (e.g., travel time) of all drivers. On the other hand, drivers are assumed to be rational and selfish, and make their travel decisions (e.g., route choices) to optimize their own travel costs, bringing the state of the network to a user equilibrium (UE). A classic approach to influence users’ route choice is using congestion tolls. In this paper, we study the SO and UE of future connected vehicular transportation networks, where users consider both the travel cost and the utility from data communication, when making their travel decisions. We leverage the data communication aspect of the decision making to influence the user route choices, driving the UE state to the SO state. We assume the cache-enabled vehicles can communicate with other vehicles via vehicle-to-vehicle (V2V) connections. We propose an algorithm for calculating the values of the data communication utility that drive the UE to the SO. This result provides a guideline on how the system operator can adjust the parameters of the communication network (e.g., data pricing and bandwidth) to achieve the optimal social cost. We discuss the insights that the results shed on a secondary optimization that the operator can conduct to maximize its own utility without deviating the transportation network state from the SO. We validate the proposed communication model via Veins simulation. The simulation results also show that the system cost can be lowered even if the bandwidth allocation does not exactly match the optimal allocation policy under 802.11p protocol.

An iterative learning approach for network contraction: Path finding problem in stochastic time‐varying networks

AbstractPath finding problem has a broad application in different fields of engineering. Travel time uncertainty is a critical factor affecting this problem and the route choice of transportation users. The major downside of the existing algorithms for the reliable path finding problem is their inefficiency in computational time. This study aims to develop a network contraction approach to reduce the network size of each specific origin and destination (OD) pair in stochastic time‐dependent networks. The network contraction is based on the comparison of optimistic and pessimistic solutions resulting from minimum and maximum travel time realizations of a Monte‐Carlo simulation (MCS)‐based approach. In this respect, the researchers propose a learning approach to utilize the information of the realizations in the initial iterations of the MCS approach. Implementation of this approach is in place for several OD pairs of two real‐world large‐scale applications. First, it is calibrated for the Chicago downtown network; the performance and accuracy of the proposed approach are investigated by comparing the results against that of the approach without any network contraction. In addition, the Salt Lake City network illustrates the transferability of the approach to other networks. The results demonstrate significant computational improvements, with an acceptable accuracy level relative to the approach without network contraction.

An online shortest path algorithm for reliable routing in schedule-based transit networks considering transfer failure probability

Most schedule-based transit assignment models assume deterministic vehicle arrival/departure times, in contrast to random vehicle arrival assumption in frequency-based models. This assumption simplifies user route choice behavior and fails to capture users’ response to unreliable service. Moreover, the inherent inconvenience of transfers, primarily due to uncertain waiting time and failure probability, has always been a challenge in modeling transit networks, to the extent that determining an appropriate transfer penalty for heterogeneous user population is still a question when using planning models. This study aims to bridge the gap between frequency-based and schedule-based transit assignment models, and proposes a path algorithm in schedule-based transit networks with stochastic vehicle arrival times to model users’ adaptive behavior in response to unreliable service. Path reliability is modeled by link failure probability, and an online shortest path algorithm is developed to find a routing policy with minimum expected travel time given preferred arrival time to destination. Complexity analyses and computational tests indicate that the model has potential for application in large-scale transit networks. Numerical tests verify that the model assigns passengers to more reliable paths with lower transfer rate without a need for a large transfer penalty.

An investigation of route-choice in integrated public transport networks by risk-averse users

Globally, transport agencies are implementing integrated public transport systems to increase ridership. Transfers are a key component of such integrated systems; they facilitate connectivity among modes, allowing the system to function in unison. It is well known that transfers can cause users to feel anxious, due to the possibility of missed connections. The public transport network in Auckland, New Zealand, is undergoing a transformation into an integrated system, which means more routes with transfers are replacing direct routes. This study examines the route choice of public transport users when faced with multiple routes involving transfers. Four sub-models are developed to create the Route Choice Model. One of the contributions of the present study is the development of the Risk-Aversion sub-model, which provides a straight-forward representation of users’ risk profiles for route-choice associated with transfers. These four sub-models are applied to a proposed integrated public transport network to determine the ridership of the new routes involving transfers. The socio-economic characteristics included in this study are gender and age. The findings show that females are more risk-averse than male users, and that users aged 40 or over are more risk-averse than those under 40. Overall, users who are more risk-averse prefer the transfer options if the service is reliable, and the transfer waiting time is relatively short in proportion to the overall journey time. Future research is encouraged to investigate the effects of other socio-economic characteristics on users’ decisions to make transfers, using the findings to make further improvements to the network.

Estimation of sparse O–D matrix accounting for demand volatility

A critical issue in origin-destination (O-D) demand estimation is under-determination: the number of O-D pairs to be estimated is often much greater than the number of monitored links. In real world, some centroids tend to be more popular than others, and only few trips are made for intro-zonal travel. Consequently, a large portion of trips will be made for a small portion of O-D pairs, meaning many O-D pairs have only a few or even zero trips. Mathematically, this implies that the O-D matrix is sparse. Also, the correlation between link flows is often neglected in the O-D estimation problem, which can be obtained from day-to-day loop detector count data. Thus, sparsity regularisation is combined with link flow correlation to provide additional inputs for the O-D estimation process to mitigate the issue of under-determination and thereby improve estimation quality. In addition, a novel strategic user equilibrium model is implemented to provide route choice of users for the O-D estimation problem, which explicitly accounts for demand and link flow volatility. The model is formulated as a convex generalised least squares problem with regularisation, the usefulness of sparsity assumption, and link flow correlation is presented in the numerical analysis.

State-Space Model for Traffic State Estimation of a Two-Dimensional Network

This study proposes a real-time monitoring method for two-dimensional (2D) networks via the fusion of probe data and a traffic flow model. In the Great East Japan Earthquake occurring on March 11, 2011, there was major traffic congestion as evacuees concentrated in cities on the Sanriku Coast. A tragedy occurred when a tsunami overtook the stuck vehicles. To evacuate safely and efficiently, the state of traffic must be monitored in real time on a 2D network, where all networks are linked. Generally, the traffic state is monitored only at observation points. However, observation data presents the risk of errors. Additionally, in the estimated traffic state of the 2D network, unlike non-intersecting road sections (i.e., one-dimensional), it is necessary to model user route choice behavior and origin/destination (OD) demand to input in the model. Therefore, in this study, we develop a state-space model that assimilates vehicle density and divergence ratio data obtained from probe vehicles in a traffic flow model that considers route choice. Our state-space model considers observational errors in the probe data and can simultaneously estimate traffic state and destination component ratio of OD demand. The result of simulated traffic model verification shows that the proposed model has good congestion estimation precision in a small-scale test network.

Flexible Emergency Vehicle Network Design considering Stochastic Demands and Inverse-Direction Lanes

We study transportation network design with stochastic demands and emergency vehicle (EV) lanes. Different from previous studies, this paper considers two groups of users, auto and EV travelers, whose road access rights are differentiated in the network, and addresses the value of incorporating inverse-direction lanes in network design. We formulate the problem as a bilevel optimization model, where the upper-level model aims to determine the optimal design of EV lanes and the lower-level model uses the user equilibrium principle to forecast the route choice of road users. A simulation-based genetic algorithm is proposed to solve the model. With numerical experiments, we demonstrate the value of deploying inverse-direction EV lanes and the computational efficiency of the proposed algorithm. We reach an intriguing finding that both regular and EV lane users can benefit from building EV lanes.

Modeling Route Choice of Utilitarian Bikeshare Users with GPS Data

To understand a bicyclist’s route choice is difficult, given the many factors that influence the attractiveness of different routes. The advent of low-cost GPS devices has made route choice analysis more precise. Bikeshare, with instrumented bikes, allows for better assessment of revealed route preference of a large subpopulation of cyclists. This study used GPS data obtained from 9,101 trips made by 1,866 users of Grid Bikeshare, Phoenix, Arizona. This unique bikeshare system relied on Social Bicycles’ onboard telematics, which allowed nonstation origins and destinations, and operated on a grid street network. The system enabled unique route choice analysis. The trips studied included only direct utilitarian trips. Circuitous trips that could have included multiple destinations or could have been recreational trips were removed. The analysis focused on facility use assessment and route choice behavior. The results were compared between two categories of bikeshare users: registered users and casual users. Registered users made shorter trips on roads with low volume and preferred bike-specific infrastructure. A path size logit model was used to model route choice. Riders were sensitive to travel distance, with little deviation from the shortest path to use more bike-friendly infrastructure. Travel on the bike-specific facilities was equivalent to a decrease in distance by 44.9% (compared with 53.3% for casual users). Left turns imposed higher disutility than right turns did for casual users. The proportion of one-way segments, annual average daily traffic values, and length of trip had a negative influence on route choice, and a number of signalized intersections had a positive influence on route selection. The results were also compared with those of earlier studies.

Optimal signal-setting for road network with maximum capacity

A signal-controlled road network with maximum capacity is considered while route choice of users is taken into account. This problem can be formulated as an optimization problem by taking user equilibrium as a constraint. A min–max bilevel program is formulated for a signal-setting problem. A new hybrid search heuristic is proposed to exploit the maximum capacity of road network using delay-minimizing signal settings. For a road network with maximum capacity, a tractable computation scheme is presented to effectively determine a delay-minimizing signal-setting. Numerical computations are performed at two benchmark signal-controlled road networks. The proposed heuristic has been demonstrated successfully to solve the min–max signal-setting problem. As compared to conventional alternatives, the proposed heuristic has been empirically demonstrated highly effective in terms of delay rate reduction.

Effect of Road Grade on Networkwide Vehicle Energy Consumption and Ecorouting

Because of growing concern about the impact of emissions from the transport sector on global climate change, vehicle energy consumption is a factor of great interest to network planners. In addition, drivers are interested in reducing energy consumption and, thus, fuel costs. However, traditional models of vehicle energy consumption have neglected an important factor: road grade. This assumption has traditionally been supported by the idea that the energy consumed because of the road grade would be reflected in changes in speed and acceleration, but a demonstration of this on an aggregate network in a city of a realistic size has been difficult to show. This work demonstrated the impact of road grade on networkwide vehicle energy consumption by the integration of energy consumption equations based on road load equations, elevation data available from the Google Elevation advanced programming interface, and a dynamic traffic assignment model to capture the effect of user route choice. This work quantified the impact of the energy consumed because of road grades on two city networks, and the results indicate that the effects of grades should not be excluded from evaluations of vehicle energy consumption. In addition, the effects of eco-routing, in which drivers choose the shortest path that consumes the least amount of energy, were explored. The results for the city networks indicate that if drivers do not account for grades, they might choose a route that actually increases vehicle energy consumption. The proposed modeling tool is scalable and easily adaptable to different cities.

Study on a user's route choice and safety consciousness in a retail power center parking lot

本研究は、地方都市の大規模店の大規模平面駐車場に着目し、歩行経路の安全性に対する利用者の意識を明らかにするとともに、歩行経路選択特性を明らかにすることによって、計画・設計時の安全性向上の必要性・重要性を示すとともに、安全性を重視する歩行者の経路選択特性を活用することで、潜在的な危険性を削減しうる経路誘導の可能性があることを明らかに使用としたものである。その結果、利用者の意識では、最短経路であることだけでなく、横断や幅員などで経路が安全であること、また安全な経路がわかりやすいことの方をより重視しており、条件によってはそういった経路の方を選択する可能性があることを明らかにした。また、歩行者は目的とする店舗入口まで距離がある場合には目的地よりもとにかく店舗建物に接近する方向の経路を選択する傾向が強く、横断歩道や誘導員を配置するなどの対策を集約的に行う場合には、こういった特性に配慮することが重要であることなどが明らかになった。

A Study on the Coordination of Urban Traffic Control and Traffic Assignment

The interactions between signal setting and traffic assignment can directly affect the urban road network efficiency. In order to improve the coordination of signal setting with traffic assignment, this paper created a traffic control algorithm considering traffic assignment; meanwhile, the link impedance function and the route choice function were introduced into this paper to study the user′s route choice and the road network flow distribution. Then based on the above research, we created a system utility value model. Finally through the VISSIM software to simulate the test network, we verified the superiority of the coordination algorithm and the model and gave the optimal flow of the road network.

An Ant Colony Optimisation algorithm for solving the asymmetric traffic assignment problem

In this paper we propose an Ant Colony Optimisation (ACO) algorithm for defining the signal settings on urban networks following a local approach. This consists in optimising the signal settings of each intersection of an urban network as a function only of traffic flows at the accesses to the same intersection, taking account of the effects of signal settings on costs and on user route choices. This problem, also known as Local Optimisation of Signal Settings (LOSS), has been widely studied in the literature and can be formulated as an asymmetric assignment problem. The proposed ACO algorithm is based on two kinds of behaviour of artificial ants which allow the LOSS problem to be solved: traditional behaviour based on the response to pheromones for simulating user route choice, and innovative behaviour based on the pressure of an ant stream for solving the signal setting definition problem. Our results on real-scale networks show that the proposed approach allows the solution to be obtained in less time but with the same accuracy as in traditional MSA (Method of Successive Averages) approaches.

Study on Regional Traffic Control Optimization Based on State Analysis

The aim of regional traffic control optimization is to find the optimal design parameters while thinking over the route choice of users. This problem can be formulated as a bi-level programming program. In the program, signal control scheme and user equilibrium traffic assignment are optimized in the upper and lower level respectively. The solution procedure developed with the genetic algorithm has been tested with an example of factual road network.Numerical experiment verified the proposed model is quite promising for use in design of regional signal control.