Path Travel Time Research Articles

Deriving Optimal Analysis Method for Road Surface Runoff with Change in Basin Geometry and Grate Inlet Installation

Road surface-runoffs have been analyzed using various approaches, but inaccurate analysis methods lead to overestimation of the drainage capacity, causing unexpected flood damage in low-lying urban areas. Previous studies have highlighted the importance of considering various parameters for accurate road surface-runoff analysis. This study involves road surface-runoff analysis using an enhanced approach considering simplified and modified drainage basin geometries, road surface and gutter flow travel times, and inlet interception efficiency as key parameters under various road conditions. The topographical road conditions were set as follows: Width (6 m), longitudinal slope of road (2–10%), road surface slope (2%), and transverse slope of gutter (2–7%). The results show that using a modified basin considering road slope conditions for estimating actual flow path length and travel time is better for flow analysis than the simplified rectangular basin. Additionally, the runoff analysis should consider the roadside inlet installation conditions, given that the bypass discharge (i.e., rainfall runoff that is not intercepted by upstream inlets) flows downstream and affects the downstream flow characteristics. Based on these results, an optimal road surface-runoff analysis method was developed, which is expected to be useful for road design.

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.

Estimation and Reliability Research of Post-Earthquake Traffic Travel Time Distribution Based on Floating Car Data

To carry out the estimation and reliability research of post-earthquake traffic travel time, which has the great influence for efficient allocation of relief materials. By analyzing the relationship among floating vehicle trajectory, target path and road network path, the intermediate parameters of converting floating vehicle trajectory data into target path travel time were defined and improved. In addition, the road damage identification method relying on lane detection is applied for evaluating the damage of road after the earthquake through the image information. Then, Bayesian average adaptive kernel density estimation method was used to estimate the distribution of post-earthquake road travel time, and a new formula for calculating the reliability of road travel time after earthquake was proposed. According to the example simulation and analysis, the proposed post-earthquake road travel time distribution estimation and its reliability are verified. The results show that when the threshold value is determined, the travel time of the path before the earthquake is the most dependable, and with the increase in the earthquake damage index, the travel time of this road section becomes increasingly unreliable. However, after the earthquake, the peak probability density of road travel time distribution weakens, and the overall probability shifts to the direction of long time.

Navigation of Multiple Robots in Formative Manner in an Unknown Environment using Artificial Potential Field Based Path Planning Algorithm

This paper proposes a new algorithm for multiple robots that can navigate from source to goal position in a coordinated and formative manner in an optimum path and time, efficiently avoiding static and dynamic obstacles. The proposed algorithm is applied here to track surveillance for two and four robots from starting point to the goal point in an obstacle environment without collision. This algorithm is inspired by the concept of the Artificial Potential Field (APF) method and behavior-based approximate reasoning. An exhaustive search approach is used to avoid the randomly appeared obstacles, approximately estimate positions of obstacles, thus reaching the surveillance point and then tracking the allotted path for the robots. The robots are governed to move in real-time, maintaining a formation between them to track the assigned path cooperatively. The performance of this algorithm is validated in a real-time and simulation environment using MATLAB/Simulink. It is observed from the results of performance measuring parameters path length and travel time that the proposed algorithm outperforms than existing A*, RRT, and GA algorithm.

A generalized Benders decomposition approach for the mean-standard deviation shortest path problem

ABSTRACT This paper concentrates on the mean-standard deviation shortest path problem, which is an important extension of traditional shortest path problem. Due to the standard deviation term, the general formulation of this problem is nonlinear and concave. We transform this formulation into a mixed-integer conic quadratic program and develop a generalized Benders decomposition approach. The Benders master problem is a continuous conic quadratic program about travel time mean and standard deviation. The subproblem is a least expected travel time path problem with the variance limit. At each iteration, the subproblem generates a generalized Benders optimality cut for the relaxed Benders master problem. The relaxed Benders master problem provides an ascending lower bound and the subproblem produces a feasible solution to update the upper bound. In the numerical experiments, all instances in four transportation networks are solved optimally. This paper provides a novel solving scheme for the mean-standard deviation shortest path problem.

Bridge seismic importance adjustment factor based on seismic risk

The current seismic design code is based on a uniform seismic hazard level rather than a uniform risk. As a result, the reliability of bridges and corresponding bridge networks cannot be guaranteed. To control the seismic risk of bridges under design requirements, the concept of bridge seismic importance adjustment factor (SIAF) is proposed to adjust bridge seismic importance factor in seismic design codes. Taking the ductility seismic design of regular bridge as an example, the relationship between seismic importance adjustment factor and seismic risk is studied. The direct loss of bridge damage and the indirect loss of increased path travel time are critical issues in the seismic design of bridges. Therefore, the seismic risk analyzed in this paper includes the risk of bridge repair cost and the risk of increased path travel time. According to the design target of seismic risk, the seismic importance adjustment factor that meets the target requirements is determined. The results show that SIAF can adjust the seismic design level of bridges to meet the requirements of the seismic risk target. In the bridge case, if the decision maker wants the risk of bridge repair cost to be controlled within 3%, SIAF greater than 1.071 can meet the seismic design requirements. If the decision maker wants the risk of path travel time of 10 bridges in series to be controlled within 6%, SIAF greater than 0.924 can meet the seismic design requirements. The proposed method of determining the seismic design level of bridges based on the seismic risk design targets can provide guidance for the formulation of bridge seismic design standards.

A new network equilibrium flow model: User-equilibrium with quantity adjustment

Travelers’ route choice behaviors affect the distribution of network flow. The paper presents an unconventional equilibrium flow model that extends the well-known non-Walrasian equilibrium theory of economics to the analysis of route choice behavior. With the introduction of path residual capacity as the quantity signal, a quantity adjustment user equilibrium flow model depicts the route choice dynamics based on the maximum path residual capacity, and thus differs from the traditional traffic equilibrium pattern regulated by path travel time. An equivalent nonlinear complementary problem to the proposed model is developed, and further reformulated as an unconstrained mathematical program using a gap function, which permits many efficient solution algorithms. The maximum residual capacity path algorithm and the maximum residual capacity traffic assignment algorithm are developed, and the gradient descent algorithm is used to solve the unconstrained mathematical program. Numerical results show that the proposed model and solution algorithm are feasible and effective. The paper proposes that both price adjustment and quantity adjustment are special cases of the price-quantity adjustment principle, and the equilibrium flow model based on which can capture route choice behaviors that has not been modeled in previous studies.

Analysing taxi customer-search behaviour using Copula-based joint model

Abstract The drivers of vacant taxis tend to cruise the road network searching new passengers, which leads to additional traffic congestion, air pollution and other problems. This study introduces a Copula-based joint model to analyse destination selection and route choice behaviour in the customer-search process. A multinomial logit model is used to analyse the destination selection behaviour, and a path size logit model is used to explore the routes choice behaviour. Accordingly, the joint model applied Copula function is then established to analyse the correlation between these two behaviours. The destination customer generation rate, destination distance, route customer generation rate, path travel time, cumulative intersection delay, path size and route length are selected as explanatory variables. The taxis trajectory data were collected by global positioning system in Xidan District of Beijing City from September 2014 to February 2015. Using the log-likelihood, Bayesian information criterion as evaluation indexes to measure the fitting result, the joint model applied Copula function has the highest goodness-of-fit. The effect of explanatory variables on customer search behaviour is discussed based on the parameter estimation results. The results of this study are helpful to understand the customer-search behaviour of taxi drivers to reduce operating costs and improve the efficiency of the taxi operation system.

Dynamic Emergency Vehicle Path Planning and Traffic Evacuation Based on Salp Swarm Algorithm

In view of the rescue delay due to traffic congestion in the urban road network, this paper implemented real-time traffic control with congestion index constraints in emergency vehicle dispatching and proposed a two-stage optimization model and algorithm. In the first stage, salp swarm algorithm (SSA) was combined with Dijkstra algorithm, and a novel hybrid algorithm with new updating rules was designed to get the multiple alternative paths. In the second stage, an improved salp swarm algorithm (ISSA) with a population grouping strategy was proposed to obtain the best evacuation schemes and the optimal rescue paths of emergency vehicles. Results of the illustrative examples show that, after evacuation, the average travel time of all alternative paths is reduced by 24.22%, while traffic congestion indexes of the adjacent road sections almost unchanged. The computation time of the hybrid algorithm for obtaining the set number of alternative paths is 56.62% and 50.47% shorter than that of bat algorithm (BA) and SSA. For the solution of the evacuation model, the computation time of the ISSA is 33.51%, 30.15%, and 30.60% shorter than that of particle swarm optimization (PSO), BA, and SSA, and the optimal solution of the ISSA is 25.92%, 10.06%, and 0.97% better than that of PSO, BA, and SSA. That is, we shorten the emergency response time and control the adverse impact of traffic evacuation on background traffic. The improved algorithm has excellent performance. This study provides a new idea and method for emergency rescue of traffic accidents.

MUSE: Multimodal Separators for Efficient Route Planning in Transportation Networks

Many algorithms compute shortest-path queries in mere microseconds on continental-scale networks. Most solutions are, however, tailored to either road or public transit networks in isolation. To fully exploit the transportation infrastructure, multimodal algorithms are sought to compute shortest paths combining various modes of transportation. Nonetheless, current solutions still lack performance to efficiently handle interactive queries under realistic network conditions where traffic jams, public transit cancelations, or delays often occur. We present a multimodal separators–based algorithm (MUSE), a new multimodal algorithm based on graph separators to compute shortest travel time paths. It partitions the network into independent, smaller regions, enabling fast and scalable preprocessing. The partition is common to all modes and independent of traffic conditions so that the preprocessing is only executed once. MUSE relies on a state automaton that describes the sequence of modes to constrain the shortest path during the preprocessing and the online phase. The support of new sequences of mobility modes only requires the preprocessing of the cliques, independently for each partition. We also augment our algorithm with heuristics during the query phase to achieve further speedups with minimal effect on correctness. We provide experimental results on France’s multimodal network containing the pedestrian, road, bicycle, and public transit networks.

Robot-Based Last-Mile Deliveries With Pedestrian Zones

Since delivery robots share sidewalks with pedestrians, it may be beneficial to choose paths for them that avoid zones with high pedestrian density. In this paper, we investigate a robot-based last-mile delivery problem considering path flexibility given the presence of zones with varying pedestrian level of service (LOS). Pedestrian LOS is a measure of pedestrian flow density. We model this new problem with stochastic travel times and soft customer time windows. The model includes an objective that reflects customer service quality based on early and late arrivals. The heuristic solution approach uses the minimum travel time paths from different LOS zones (path flexibility). We demonstrate that the presence of pedestrian zones leads to alternative path choices in 30% of all cases. In addition, we find that extended time windows may help increase service quality in zones with high pedestrian density by up to 40%.

Data‐Driven Method for Passenger Path Choice Inference in Congested Subway Network

In a congested large‐scale subway network, the distribution of passenger flow in space‐time dimension is very complex. Accurate estimation of passenger path choice is very important to understand the passenger flow distribution and even improve the operation service level. The availability of automated fare collection (AFC) data, timetable, and network topology data opens up a new opportunity to study this topic based on multisource data. A probability model is proposed in this study to calculate the individual passenger’s path choice with multisource data, in which the impact of the network time‐varying state (e.g., path travel time) on passenger path choice is fully considered. First, according to the number and characteristics of OD (origin‐destination) candidate paths, the AFC data among special kinds of OD are selected to estimate the distribution of passengers’ walking time and waiting time of each platform. Then, based on the composition of path travel time, its real‐time probability distribution is formulated with the distribution of walking time, waiting time, and in‐vehicle time as parameters. Finally, a membership function is introduced to evaluate the dependence between passenger’s travel time and the real‐time travel time distribution of each candidate path and take the path with the largest membership degree as passenger’s choice. Finally, a case study with Beijing Subway data is applied to verify the effectiveness of the model presented in this study. We have compared and analysed the path calculation results in which the time‐varying characteristics of network state are considered or not. The results indicate that a passenger’s path choice behavior is affected by the network time‐varying state, and our model can quantify the time‐varying state and its impact on passenger path choice.

OPTIMIZING THE CONTAINER TRUCK PATHS WITH UNCERTAIN TRAVEL TIME IN CONTAINER PORTS

The yard template problem in container ports determines the assignment of space to store containers for the vessels, which could impact container truck paths. Actually, the travel time of container truck paths is uncertain. This paper considers the uncertainty from two perspectives: (1) the yard congestion in the context of yard truck interruptions, (2) the correlation among adjacent road sections (links). A mixed-integer programming model is proposed to minimize the travel time of container trucks. The reliable shortest path, which takes the correlation among links into account is firstly discussed. To settle the problem, a Shuffled Complex Evolution Approach (SCE-UA) algorithm is designed to work out the assignment of yard template, and the A* algorithm is presented to find the reliable shortest path according to the port operator’s attitude. In our case study, one yard in Dalian (China) container port is chosen to test the applicability of the model. The result shows the proposed model can save 9% of the travel time of container trucks, compared with the model without considering the correlation among adjacent links.

Dynamic Multipath Signal Progression Control Based on Connected Vehicle Technology

In recent years, connected vehicle (CV) technology has reached a level of maturity and can be beneficial for traffic control at signalized intersections. Enriched information provided by CVs could be utilized to perform a more efficient signal control. This paper proposes a methodology to dynamically adjust the offsets to provide progression bands for multiple critical paths in a CV environment. The critical paths refer to the routes connecting origin-destination (OD) pairs with the highest volumes and are determined based on the CV trajectory. A real-time optimization model is constructed to design a coordination plan, and the control objective is to provide maximum green bandwidth along the determined critical paths by optimizing the offsets of all intersections along an arterial. To solve this model, a solution algorithm based on dynamic programming is proposed. A real-world arterial in Salt Lake City, Utah, is modeled in commercially available software to evaluate the effectiveness and efficiency of the proposed control strategy. Simulations are conducted to compare the proposed system with a fixed coordination strategy. The results reveal that the travel times of those critical paths by the proposed coordination strategy are lower the ones by the fixed coordination strategy. The average delay and average number of stops of critical paths are reduced by about 16.82% and 5.94%, respectively, compared with the fixed coordination strategy. Moreover, results also indicate that the proposed coordination strategy outperforms the fixed coordination strategy by reducing 7.84% and 5.79% of the average delay and average stops, respectively.

Decentralised cooperative cruising of autonomous ride-sourcing fleets

As transportation network companies and automobile manufacturers continue to invest in the development of self-driving vehicles, it can be expected that autonomous taxi (a-taxi) fleets will become a major component of on-demand transport services in the foreseeable future. The majority of existing automated fleet management systems focus on central dispatch strategies that rely on real-time information and communication. This paper proposes a novel decentralised cooperative cruising method for offline operation of a-taxi fleets, which serves as a contingency plan during a full communication shutdown. The proposed method acts as an emergency plan for the system to continue serving passengers with the objective of maximising the total number of served passengers by the fleet. The method uses historical trip data to estimate PageRank centralities of roads as a proxy of long-term likelihood of finding waiting passengers over a series of trips. The proposed method uses this metric to (i) compute weighted shortest paths for vacant a-taxi cruising route planning, and (ii) partition the network into homogeneous regions for effective cruising destination choice (mission planning). The movements of vacant a-taxis between regions are modelled as a Markov chain such that a transition probability matrix is computed to achieve the optimal spatial distribution of vacant a-taxis to maximise the total expected pick-ups by the fleet, estimated based on bilateral meeting functions. Compared to benchmark strategies which select destinations randomly and cruise along the shortest travel time path, the proposed method shows significant improvements in service performances for different fleet sizes.

Incorporating Travel Time Reliability in Equitable Congestion Pricing Schemes for Heterogeneous Users and Bimodal Networks

Congestion pricing is proposed as an effective travel demand management strategy to circumvent the problem of congestion and generate revenue to finance developmental projects. There are several studies focusing on optimal pricing strategies to minimize the congestion level or maximize the revenue of the system. However, with regard to equity issues, benefiting only users with higher value of time is claimed to be the main factor that prevents implementation of such policies in practice. While many studies aimed to tackle the equity issues by certain welfare analyses, most of these studies fail to fully consider realistic features of users’ behavior and the uncertainty in link travel times. Given the variability of travel time in real-world networks and the impacts of pricing policies on path travel time distributions, it is important to consider the users’ reliability valuations, in addition to their travel time valuations. Thus, the goal in this study is to find an equitable pricing scheme that minimizes the total travel time of auto users in a general bimodal network considering heterogeneous users with different values of time and reliability. A particle swarm optimization algorithm is proposed to find self-funded and Pareto-improving optimal toll values. A reliability-based user equilibrium algorithm is embedded into this optimization algorithm to assign travelers to the equilibrated paths for different user classes given toll values. The proposed approach is successfully applied to a modified Sioux Falls network to explore impacts of subsidization, congestion level, and considering travel time reliability on the pricing strategy and its effectiveness.

Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

A novel approach for vehicle travel time distribution: copula-based dependent discrete convolution

ABSTRACT Travel time reliability is considered as one of the key indicators for the performance of transportation systems. The majority of studies concerning estimating arterial travel time distribution commonly assume that the path travel time follows a certain distribution without considering segment correlation. Therefore, convolution is used. However, the assumption of independence of travel times on successive segments may not be appropriate. Recent studies showed that copulas are able to capture segment correlation. These copula models, however, are unfeasible in real-world applications. This paper proposes a novel approach using dependent discrete convolution. Path-level travel time distribution is estimated by aggregating segment-level travel time distributions using copula-based discrete convolution. This estimation is compared to the estimation of traditional convolution, a state-of-the art copula model, and the empirical distribution. It is shown that the proposed methodology produces accurate results within feasible computational time, thus, making it eligible for real-world applications.

RFID E-plate Data based Travel Time Reliability Estimation of Urban Expressway

Travel time distribution shows the aggregation degree of vehicle travel time in road network, and travel time reliability is the key index to measure the aggregation degree of travel time. In order to quantitatively evaluate the fluctuation of travel time of expressway vehicles, it is necessary to establish an estimation method of travel time reliability of expressway. Travel time reliability estimation needs to obtain travel time data of vehicles. Traditional Global Position System (GPS) and video detection methods are easily affected by external factors such as weather and environment, and the description of travel time distribution is not comprehensive under the condition of low permeability. On the other hand, in the evaluation of travel time reliability, although there are lots of travel time probability distribution models, the correlation of vehicle travel time between links is ignored when calculating travel time reliability. In order to estimate travel time reliability more accurately, based on Radio Frequency Identification (RFID) electronic license plate (E-plate) data, this paper proposes a path travel time reliability evaluation model considering travel time correlation between links. First, the RFID readers installed on the gantry of expressway acquires data from the electronic tags deployed on vehicles. Secondly, a percentile travel time estimation method is proposed based on Cornish-Fisher expansion and the skewness of travel time distribution. Finally, considering the reliability estimation of travel time under the correlation of links, the estimated value is closer to the true value. The experimental results show that the travel time reliability estimation method considering the correlation between links improves the accuracy of estimation in the expressway network environment.

Depth-Dependent Shear-Wave Attenuation in Central Apennines, Italy

We used 1029 earthquakes, with magnitudes ranging from M 3.0 to M 6.5, located in central Apennines, Italy, and recorded by 414 local stations to study the variation of the quality factor QS of shear waves with depth. We first determined average nonparametric attenuation functions in the frequency band from 0.5 to 20 Hz and hypocenter distances less than 155 km to correct the observed acceleration spectra for attenuation effects. Then, we separated source and site effects from the corrected spectral records to determine the changes of QS with depth. We used a 1D local shear-wave velocity model to calculate the travel times of the source-station paths, and we inverted the observed spectra to determine QS in three different depth intervals (0–4 km, 4–10 km and 10–15 km) and five frequencies (0.5, 1, 4, 10 and 20 Hz). We found that QS increases with frequency at all depths considered and tends to have lower values at shallow depths. The average value of QS is consistent with previous studies made in central Italy and can be approximated by QS = 43f0.94. To describe the frequency dependence of QS with depth (H), we determine the following relations: QS = 5.5f1.39, 0.5 ≤ f ≤ 10 Hz and QS = 151.5, f > 10 Hz for 0–4 km, QS = 52f0.87 for 4 < H < 10 km and QS = 51f0.92 for 10 ≤ H ≤ 15 km. We conclude that the Q-depth-dependent model can be useful to improve estimates of source parameters and ground motion prediction in the central Apennines region of Italy.