Road Network Travel Time Research Articles

Estimation of stochastic link capacity and link performance function including uncertainty of driver’s behaviour

ABSTRACT Recently, user equilibrium models with uncertainty were proposed to describe stochastic travel time in road networks. The accurate estimation of a stochastic link capacity is important for such models. This study develops a method for estimating a stochastic link capacity by considering the uncertainty of the driver’s behaviour. We postulate that the stochastic link capacity follows a lognormal distribution. The characteristics of the lognormal distribution suit real traffic observation, e.g. nonnegativity, asymmetricity, and long-tailed. Furthermore, the estimated link capacity can derive the analytical likelihood function for the link performance function. Then, the link performance function can be estimated analytically by explicitly considering the source of uncertainty. The proposed methods can contribute to the estimation of stochastic link capacity and the link performance function for the user equilibrium model with stochastic travel time. A numerical calculation demonstrates the proposed method using real traffic observations in Sapporo, Japan.

Assessing the spatial accessibility and spatial equity of public libraries' physical locations

Spatial accessibility of libraries affects their usage. It is crucial to consider spatial accessibility's impacts on equity and inclusiveness of public libraries. A method is proposed to evaluate the equity of spatial accessibility to library resources and services across population groups. Spatial accessibility is measured in two ways: the closest-based and quantity-based measurements that respectively characterize the level of accessibility to the closest one and to diverse resources according to travel distance and travel time in road networks. Two approaches are applied for equity evaluation with socioeconomic and demographic indicators of census tracts, including the minority population, non-adults, and population in poverty. The method is applied to examine the physical locations of public libraries in four major cities of the U.S with diverse population composition. Results show spatial inequity for socially disadvantaged populations in the cities of Washington DC, Baltimore, and Chicago. Inequity is more prominent for non-adults and minority populations in DC and Baltimore. The analysis helps identify the potential inequity for subpopulations and guide the decision-making in library services to improve equity and inclusion.

Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction

In order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. Due to the highly nonlinear and dynamic nature of travel time series, it is necessary to consider time dependence and the spatial reliance of travel time series for predicting the travel time of road networks. The dynamic feature of the travel time series can be revealed by the EDM method, a nonlinear approach based on Chaos theory. Further, the spatial characteristic of urban traffic topology can be reflected from the perspective of complex networks. To fully guarantee the reasonability and validity of spatio-temporal features, which are dug by empirical dynamic modeling and complex networks (EDMCN), for urban traffic travel time prediction, an XGBoost prediction model is established for those characteristics. Through the in-depth exploration of the travel time and topology of a particular road network in Guiyang, the EDMCN-XGBoost prediction model’s performance is verified. The results show that, compared with the single XGBoost, autoregressive moving average, artificial neural network, support vector machine, and other models, the proposed EDMCN-XGBoost prediction model presents a better performance in forecasting.

The varying effects of accessing high-speed rail system on China’s county development: A geographically weighted panel regression analysis

The construction of high-speed rail in China was initiated to answer increasing demand for fast and convenient transportation systems connecting large economic centers. After the first high-speed rail was open for operation and the initial adjustment period, access to high-speed rail starts to bring fundamental changes in regional economic operation modes in China and drastically increase interconnectivity among places that are used to be farther apart. It is commonly understood that access to HSR will have significant impact on economic development. It is, however, also quite possible that the benefits to economic development brought by HSR will have a diminishing marginal effect. That is to say, the benefits brought by HSR to economic development tend to be the greatest when access to HSR is scarce. The benefits will decrease once access to HSR becomes more frequent. With data of HSR stations distribution and a set of panel data of socioeconomic information at county-level from 2008 – 2015 in China, this study creates four HSR accessibility indices and attempts to provide insights on how access to the HSR system supports China’s county-level development. The first one simply measures the geographic distributions of HSR stations and feed the data to a global spatial panel model to investigate whether the presence of an HSR station will have significant impact on county development. The second one directly measures the accessibility to HSR based on road network travel time. The third one measures a Euclidean distance from the geometric center of the county to the nearest HSR station. The fourth one is an inclusive and inversed distance measure attempting to capture HSR’s geographic influence. The last three indices will be used in a geographically weighted panel regression model to test the potential varying relationships between HSR accessibility and county development, controlling other socioeconomic factors. Our results suggest that on average the presence of an HSR station suggests about 2.7 % increase of that county’s per capita GDP. The geographically weighted panel regression suggests that in places where HSR is sparsely distributed (access to HSR is scarce and less frequent), the relationship between HSR accessibility and GDP per capita is significant and positive. In places where HSR is densely distributed (access to HSR is more frequent), the relationship is less apparent. The current study explores the distribution of HSR and its contribution to county development in China. We hope the results will offer significant insights of the relationships between infrastructure construction and county economic development in both China and beyond.

Reliable shortest path finding in stochastic time-dependent road network with spatial-temporal link correlations: A case study from Beijing

In view of the time-dependent characteristic of travel times in road networks and the travel time reliability (TTR) requirements by different travelers, it is complicated and time-consuming to determine the reliable shortest path (RSP) in large-scale road networks. To search the RSP in stochastic and time-dependent (STD) network with spatial-temporal correlated link travel times, an efficient path finding algorithm is presented. First, the fitting test results based on floating car data show that it is more appropriate to characterize the travel time distributions (TTDs) of links using lognormal distributions. In order to quantify spatial-temporal correlations between links, correlation coefficients of link travel times are calculated. Also, influences of spatial distance (counted by the number of links), temporal distance (counted by the number of time intervals) and road type on link correlations is analyzed. Afterwards, the dynamic moment-matching method (DMM) is used to calculate the approximate path TTD when correlated link travel times are considered. Accounting for different travelers' risk tolerance, a dynamic-moment-matching-based A* algorithm (STCRSP-DMA*) is proposed to provide personalized path navigation for individual travelers. Last, numerical case studies based on abundant floating car data as well as a subsistent road network in Beijing are conducted to demonstrate the applicability and the computational advantage of the devised algorithm in solving RSP searching problems.

Integrated framework for real‐time urban network travel time prediction on sparse probe data

The study presents the methodology and system architecture of an integrated urban road network travel time prediction framework based on low-frequency probe vehicle data. Intended applications include real-time network traffic management, vehicle routing and information provision. The framework integrates methods for receiving a stream of probe vehicle data, map matching and path inference, link travel time estimation, calibration of prediction model parameters and network travel time prediction in real time. The system design satisfies three crucial aspects: computational efficiency of prediction, internal consistency between components and robustness against noisy and missing data. Prediction is based on a multivariate hybrid method of probabilistic principal component analysis, which captures global correlation patterns between links and time intervals, and local smoothing, which considers local correlations among neighbouring links. Computational experiments for the road network of Stockholm, Sweden and probe data from taxis show that the system provides high accuracy for both peak and off-peak traffic conditions. The computational efficiency of the framework makes it capable of real-time prediction for large-scale networks. For links with large speed variations between days, prediction significantly outperforms the historical mean. Furthermore, prediction is reliable also for links with high proportions of missing data.

Validation of commercial business lists as a proxy for licensed alcohol outlets

BackgroundStudies of retail alcohol outlets are restricted to regions due to lack of U.S. national data. Commercial business lists (BL) offer a possible solution, but no data exists to determine if BLs could serve as an adequate proxy for license data. This paper compares geospatial measures of alcohol outlets derived from a commercial BL with license data for a large US state.MethodsWe validated BL data as a measure of off-premise alcohol outlet density and proximity compared to license data for 5528 randomly selected California residential addresses. We calculated three proximity measures (Euclidean distance, road network travel time and distance) and two density measures (kernel density estimation and the count within a 2-mile radius) for each dataset. The data was acquired in 2015 and processed and analyzed in 2015 and 2016.ResultsCorrelations and reliabilities between density (correlation 0.98; Cronbach’s α 0.97–0.99) and proximity (correlations 0.77–0.86; α 0.87–0.92) measures were high. For proximity, BL data matched license in 55–57% of addresses, overstated distance in 19%, and understated in 24–26%.ConclusionsBL data can serve as a reliable proxy for licensed alcohol outlets, thus extending the work that can be performed in studies on associations between retail alcohol outlets and drinking outcomes.

Dynamic Short-Term Projections of Travel Time Distributions in Urban Signalized Networks utilizing Composite Information of Traffic Characteristics

Abstract: The estimation of experienced travel-time in road networks stands for an important feature in Advanced Traveler Information Systems (ATIS). In the era of data availability, the dissemination of accurate traffic information to travelers could have a huge impact on their trip choices and thus in systems’ performance. The scope of this paper is time travel modeling and prediction based on alternative configurations of mixed duration and time series models. The proposed framework enables the dynamic probabilistic estimation of travel time between a predefined set of Origin-Destinations (O-D) locations, by taking into account available spatial data. For achieving that, suitable formulated hazard models are combined with time series input, forming a hybrid methodological framework and compared to two different theoretical duration models. The resulted mechanism provides valuable probabilistic estimations for travel time for O-D pairs, able to cover extensive urban road networks. In particular, the proposed modeling approach uses past travel time observations as inputs and provides distributions of future travel times. The proposed application is tested on a realistic road network, namely that of Cyprus. The aim of the study is to provide reliable travel information to travelers in a methodologically sound, straightforward and comprehensible way.

Research on Link Travel Time Reliability Model Based on Massive Expressway Toll Data

Massive expressway toll data contained lots of valuable information. However, the skills of mining and analyzing toll data were limited currently. This study explored the modeling method of road network travel time reliability based on massive toll data. Firstly, this study obtained travel time data sample of each link at different months, and analyzed travel time statistical properties preliminarily. Secondly, this study used normal distribution, gamma distribution and Weibull distribution to fit travel time data sample, and different statistical indicators were involved to measure the fitting effect. Fitting results showed that normal distribution for link travel time was more rational and acceptable than the others. Thus, this study established link travel time reliability model, and proposed moment estimation method of calibrating the model parameters. In practical application, the reliability model can be used to judge traffic operating posture for expressway management department, and also can be used to forecast travel time information, to provide valuable reference on decision-making for drivers travel plan or route choice.

Shortest Path Finding Problem in Stochastic Time-Dependent Road Networks With Stochastic First-In-First-Out Property

As travel times in road networks are dynamic and uncertain, it is difficult and time-consuming to search for the least expected time path in large-scale networks. This paper addresses the problem of finding the least expected time path in stochastic time-dependent (STD) road networks. A stochastic travel speed model is proposed to represent STD link travel times. It is proved that the link travel times in STD networks satisfy the stochastic first-in-first-out (S-FIFO) property. Based on this S-FIFO property, an efficient multicriteria A* algorithm is proposed to exactly determine the least expected time path in STD networks. Computational results using several large-scale road networks show that the proposed algorithm has a significant computational advantage over existing solution algorithms without the S-FIFO property.

Accuracy of distance‐based travel time decomposition in probe vehicle systems

SUMMARYProbe vehicle data (PVD) are commonly used for area‐wide measurements of travel time in road networks. In this context, travel times usually refer to fixed edges of an underlying (digital) map. That means measured travel times have to be transformed into so‐called link travel times first. This paper analyzes a common method being applied for solving this task (distance‐based travel time decomposition). It is shown that, in general, its inherent imprecision must not be neglected. Instead, it might cause a serious misinterpretation of data if potential errors in the context of travel time decomposition are ignored. For this purpose, systematic as well as maximum deviations between “decomposed” and “true” link travel times are mathematically analyzed. By that, divergent statements in the literature about the accuracy of PVD are harmonized. Moreover, conditions for the applicability of the so‐called distance‐proportion method are derived depending on the permitted error level. Three examples ranging from pure theory to real world confirm the analytical findings and underline the problems resulting from distance‐based travel time decomposition at local level, for example, at individual intersections. Copyright © 2013 John Wiley & Sons, Ltd.

Travel time estimation for urban road networks using low frequency probe vehicle data

The paper presents a statistical model for urban road network travel time estimation using vehicle trajectories obtained from low frequency GPS probes as observations, where the vehicles typically cover multiple network links between reports. The network model separates trip travel times into link travel times and intersection delays and allows correlation between travel times on different network links based on a spatial moving average (SMA) structure. The observation model presents a way to estimate the parameters of the network model, including the correlation structure, through low frequency sampling of vehicle traces. Link-specific effects are combined with link attributes (speed limit, functional class, etc.) and trip conditions (day of week, season, weather, etc.) as explanatory variables. The approach captures the underlying factors behind spatial and temporal variations in speeds, which is useful for traffic management, planning and forecasting. The model is estimated using maximum likelihood. The model is applied in a case study for the network of Stockholm, Sweden. Link attributes and trip conditions (including recent snowfall) have significant effects on travel times and there is significant positive correlation between segments. The case study highlights the potential of using sparse probe vehicle data for monitoring the performance of the urban transport system.

Impact of Traveler Information on Road Network Travel Time Reliability

To investigate the provision of traffic information on road network travel time reliability, the travelers are divided into two classes: travelers equipped with advanced traveler information systems (ATIS) and travelers unequipped with ATIS. A route choice behavior model considering information guidance is formulated as a mathematical program based on the mixed stochastic user equilibrium modeling approach in conjunction with the assumption is that two classes of travelers made their route choices in a Logit-based stochastic manner. The link capacities are treated as truncated normal random variables considering the fact that link capacity can only vary between 0 to link design capacity. With the above considerations, a travel time reliability evaluation method with degradable link capacity under influence of traveler information is proposed using the Monte Carlo simulation technique and network equilibrium flow solution algorithm. Some numerical studies on a small road network are conducted to validate the proposed method, the results show that: road network travel time reliability increases with the quality of information and/or the market penetration of ATIS, but the marginal effects of them are decreasing; for a road network with more higher traffic demand, the provision of traveler information can significantly improve road network travel time reliability.

Comparison of MATSim and EMME/2 on Greater Toronto and Hamilton Area Network, Canada

The agent-based microsimulation modeling technique for transportation planning is rapidly developing, is being applied in practice, and is attracting considerable attention. Along with the conventional four-step modeling technique, MATSim and EMME/2 represent two genres of traffic assignment. They are built on different theoretical bases: dynamic stochastic stationary state assignment and static deterministic user equilibrium assignment, respectively. A study was done of the models' application with data from the Greater Toronto and Hamilton area network in Canada. Given the actual demand data, the models' assignment results are compared and validated on the basis of four indicators of the road network—travel time, travel distance, link volume, and link speed—to reflect both spatial and temporal variation of the traffic flow pattern. The comparison results show that numerical outputs produced by MATSim are not only compatible with those by EMME/2 but are also more realistic from a temporal point of view. The agent-based microsimulation model can be an appropriate alternative to the conventional model for transportation planning. Therefore, agent-based microsimulation models reflect a promising direction of next-generation transportation planning models.

Price of Anarchy in Transportation Networks: Efficiency and Optimality Control

Uncoordinated individuals in human society pursuing their personally optimal strategies do not always achieve the social optimum, the most beneficial state to the society as a whole. Instead, strategies form Nash equilibria which are often socially suboptimal. Society, therefore, has to pay a price of anarchy for the lack of coordination among its members. Here we assess this price of anarchy by analyzing the travel times in road networks of several major cities. Our simulation shows that uncoordinated drivers possibly waste a considerable amount of their travel time. Counterintuitively, simply blocking certain streets can partially improve the traffic conditions. We analyze various complex networks and discuss the possibility of similar paradoxes in physics.

Influence of peak spreading and departure time choice on evaluation of its measures

AbstractIntelligent transport systems provide various means to improve capacity and travel time in road networks. Evaluation of the benefits of these improvements requires consideration of travellers' response to them. We consider a continuous‐time equilibrium model of departure time choice and identify a formula for the dynamic equilibrium departure rate profile. We develop the analysis to consider the effect on the cost incurred by travellers of ITS measures through their effects on each of the travel time in the absence of congestion, and the capacity for travel. This shows the importance in choice of departure time of travellers' values of time at each of the origin and destination of their journeys. We show the importance of these values of time in evaluation, and that if travellers value their time at both the origin and destination of their journeys, their responses will lead them to achieve a greater reduction in costs than would be achieved under free‐flow conditions.

Variances of link travel time estimates: implications for optimal routes

AbstractIn this paper, we explore the consequences of using link travel time estimates with high variance to compute the minimum travel time route between an origin and destination pair. Because of platoon formation or for other reasons, vehicles on a link separated by small headways tend to have similar travel times. In other words, the covariance of link travel times of distinct vehicles which are close together may not be zero. It follows that the variance of the mean of travel times obtained from a sample of n vehicles on a same link over small time intervals is of the form a+b/n where a and b would usually be positive. This result has an important implication for the quality of road network travel time information given by Intelligent Transportation Systems (ITS)—that the variance of the estimate of mean travel time does not go to zero with increasing n. Thus the quality of information disseminated by ITS is not necessarily improved by increasing the market penetration of vehicles monitoring the system with the necessary equipment (termed probe vehicles). Estimates of a and b for a set of links are presented in the paper and consequences for probe‐based ITS are explored by means of a simulation of such a system which is operational on an actual network.

Variances of link travel time estimates: implications for optimal routes

In this paper, we explore the consequences of using link travel time estimates with high variance to compute the minimum travel time route between an origin and destination pair. Because of platoon formation or for other reasons, vehicles on a link separated by small headways tend to have similar travel times. In other words, the covariance of link travel times of distinct vehicles which are close together may not be zero. It follows that the variance of the mean of travel times obtained from a sample of n vehicles on a same link over small time intervals is of the form a+ b/ n where a and b would usually be positive. This result has an important implication for the quality of road network travel time information given by Intelligent Transportation Systems (ITS)—that the variance of the estimate of mean travel time does not go to zero with increasing n. Thus the quality of information disseminated by ITS is not necessarily improved by increasing the market penetration of vehicles monitoring the system with the necessary equipment (termed probe vehicles). Estimates of a and b for a set of links are presented in the paper and consequences for probe-based ITS are explored by means of a simulation of such a system which is operational on an actual network.

Travel Time Prediction in Urban Road Networks

Abstract The prediction of travel times in road networks is a basic component of many traffic monitoring and control systems. This paper extends the simple queueing model described in Kimber and Daly (1986) to use the value of the detector occupancy per cycle, in addition to the vehicle count, in order to predict link travel times from readily-available detector inputs. The model was fitted to simulated data from the VISSIM microscopic traffic simulation tool and performed well in validation on an independently generated data-set. Tests with SCOOT data gathered by the Instrumented City facility were also encouraging.