Experienced Travel Times Research Articles

Fixed-Time Traffic Control at Superstreet Intersections by Bee Colony Optimization

The superstreet intersection (or restricted crossing U-turn-, J-turn intersection) fixed-time traffic control system was developed in this study. The optimal (or near-optimal) values of cycle length, splits, and offsets were discovered by minimizing the experienced travel time of all network users traveling through the superstreet intersection. The optimization procedure used was based on the bee colony optimization (BCO) metaheuristic. The BCO is a stochastic, random-search, population-based technique, inspired by the foraging behavior of honey bees. The BCO belongs to the class of swarm intelligence methods. A set of numerical experiments was performed. Superstreet intersection configurations that allowed direct left turns from the major street, as well as configurations with no direct left turns, were analyzed within numerical experiments. The obtained results showed that BCO outperformed the traditional Webster approach in the superstreet geometrical configurations considered.

Improving Interstate Freeway Travel Time Reliability Analysis by Clustering Travel Time Distributions

Travel time reliability quantifies variability in travel times and has become a critical aspect for evaluating transportation network performance. The empirical travel time cumulative distribution function (CDF) has been used as a tool to preserve inherent information on the variability and distribution of travel times. With advances in data collection technology, probe vehicle data has been frequently used to measure highway system performance. One challenge with using CDFs when handling large amounts of probe vehicle data is deciding how many different CDFs are necessary to fully characterize experienced travel times. This paper explores statistical methods for clustering CDFs of travel times at segment level into an optimal number of homogeneous clusters that retain all relevant distributional information. Two clustering methods were tested, one based on classic hierarchical clustering and the other used model-based functional data clustering, to find out their performance on clustering distributions using travel time data from Interstate 64 in Virginia. Freeway segments and those within interchange areas were clustered separately. To find the proper data format as clustering input, both scaled and original travel times were considered. In addition, a non-data-driven method based on geometric features was included for comparison. The results showed that for freeway segments, clustering using travel times and the Anderson–Darling dissimilarity matrix and Ward’s linkage had the best performance. For interchange segments, model-based clustering provided the best clusters. By clustering segments into homogenous groups, the results of this study could improve the efficiency of further travel time reliability modeling.

System optimal routing of traffic flows with user constraints using linear programming

For static traffic assignment problems, it is well known that (1) for some users the experienced travel time in a system optimum assignment can be substantially higher than the experienced travel time in a user equilibrium assignment, and (2) the total travel time in user equilibrium can be substantially higher than the total travel time in system optimum. By seeking system optimal traffic flows subject to user constraints, a compromise assignment can be obtained that balances system and user objectives. To this aim, a linear model and an efficient heuristic algorithm are proposed in this paper. A computational study shows that the proposed model, along with the heuristic algorithm, is able to provide fair solutions with near-optimal total travel time within very short computational time.

Comparing values of travel time obtained from workplace and short-term decisions

The value of travel time is an important element in cost-benefit analysis of transportation projects, by encapsulating the willingness to pay of the population for improvements in the transport system. Those values are typically obtained from mobility behavior data from revealed or stated preference surveys. Although short-term decisions are typically used for this purpose, a growing number of authors is arguing that long-term decisions might provide more meaningful values for the evaluation of transportation projects, as those decisions have a longer-lasting effect on the experienced travel times. This paper uses data which contains both short- and long-term experiments to investigate the impact of different time horizons on the valuation of time. Using a joint model including all short term, together with workplace choice situations, the differences in the valuation of time coming from different kinds of experiments are investigated. The results reveal difficulties to isolate the willingness to pay from other aspects of monetary compensation, such as income as a status or career progression symbol. The results confirm that the chosen time horizon has a significant effect on the valuation of travel time and cost. The richer data, compared to previous attempts at such an estimation, does reveal, however, potential pitfalls in the underlying theory, namely that there is a perfect substitution between travel costs and income. In particular, focusing on salary neutral situations instead of situations with salary gains changes the direction of the effect of long term decisions. Recommendations are derived for forthcoming studies.

Adaptive Rolling Smoothing With Heterogeneous Data for Traffic State Estimation and Prediction

Spatial–temporal traffic state estimation and the prediction of urban expressways is a vital component of traffic management and information systems. The adaptive smoothing method is one of the most frequently used approaches to estimate traffic states. However, the fixed filter parameters used in existing approaches sometimes fail to characterize traffic dynamics well. To better capture generation, propagation, and mitigation dynamics of traffic congestion, we propose an adaptive rolling smoothing (ARS) approach by dynamically tuning the filter parameters in a rolling horizon scheme for online applications. The fusion of heterogeneous traffic data combines aggregate traffic measurements (e.g., traffic flow rate, time occupancy, and speed collected by remote microwave sensors) and disaggregate information (e.g., timestamps of individual vehicles detected by license plate recognition cameras). A nonlinear traffic flow filter based on the virtual trajectory algorithm is established to reconstruct the spatial–temporal traffic state and estimate experienced travel times of individual vehicles. The results demonstrate the capability and effectiveness of the proposed ARS approach in the historical traffic state estimation and short-term traffic flow prediction. Complicated traffic states of weaving, merging, and diverging segments can be well distinguished by reconstructing time-space speed diagrams. The proposed approach can be extended to develop efficient missing data imputation algorithms and hierarchical control strategies for heterogeneously congested urban expressways.

Agent-Based Dynamic Traffic Assignment with Information Mixing

This study develops an approach for network assignment convergence using individualized agent-based routing and agent-specific link cost information as well as historical travel times. Each traveler is routed individually from their starting and ending network link at a specific departure time. The approach is gap-based in two ways. First, as in previous approaches, the re-assignment decision during the convergence process is based on the gap between the routed and experienced travel time from the previous iteration. Secondly, the historical time-dependent and prevailing traffic conditions are averaged into a single expected value for each agent using a weight calculated by a modified two-parameter Weibull survival function. This weight is individualized based on the relative gap of the traveler from the previous iteration, as well as the iteration number; a novel aspect of this work. The methodology is tested on a medium-scale network of Bloomington, IL. The algorithm converges after only two iterations, which is promising as computational time of a single iteration can be high for large-scale networks.

Operational Effects of the Displaced Partial Cloverleaf Interchange

As the roadway infrastructure in the United States evolves, transportation agencies continue to seek effective interchange alternatives that can accommodate site-specific needs, such as high through and turning volumes, or optimized traffic signal operations (by minimizing the number of traffic signal phases). In recent years, innovative interchanges such as the diverging diamond have enabled transportation agencies to explore unique designs compatible with their specific needs. This paper introduces a new innovative interchange, known as a displaced partial cloverleaf (DPC) interchange. This unique interchange is characterized by a single intersection, six free-flow movements, only four movements controlled by the solitary traffic signal, an intersection location that can be shifted, and only 12 conflict points. The flexible intersection location can help an agency improve corridor progression, eliminate conflicting queues from nearby intersections, or enhance/maintain the bridge structure without compromising the intersection operations. A microsimulation study was conducted to compare the operational performance of a DPC interchange to that of a four-quadrant type B partial cloverleaf (PARCLO B-4Q) interchange, which is very similar in layout to a DPC interchange. Results show significant decreases in experienced travel time for left-turning vehicles with the DPC. The DPC interchange is recommended as an alternative option for interchanges with very heavy left-turning onramp demand in combination with heavy crossroad demand.

Utilizing data mining techniques to predict expected freeway travel time from experienced travel time

As the most important real-time traveler information, travel time can be either experienced or expected (i.e. to be experienced). When a vehicle completes a trip, the travel time refers to the experienced travel time. In contrast, when a vehicle starts its journey, the travel time is unknown but can be predicted, which is the expected travel time. Although the experienced travel time is termed as the real-time travel time, a traveler may encounter a somewhat different travel time (from expected travel time) due to the changing traffic conditions. Therefore, expected travel time needs to be predicted. In this study, the expected travel time was predicted from the experienced travel time using the data mining techniques such as k-nearest neighbor (k-NN), least squares regression boosting (LSBoost) and Kalman filter (KF) methods. After comparing the performances of KF to corresponding modeling techniques from both link and corridor perspectives, it is concluded that the KF method offers superior prediction accuracy in a link-based model. Moreover, the effect of different noise assumptions was examined and it is found that the steady noise computed from the full-dataset had the most accurate prediction. A data processing algorithm, which processed more than a hundred million records reliably and efficiently was also introduced.

Examining the Relationship between Drivers’ Anticipated Travel Time and Previous Experienced Travel Times

AbstractTravel time reliability refers to the day-to-day variability of trip travel times. There is a belief that there is a cost associated with unreliability and this cost can be quantified as a ...

Probe data-driven travel time forecasting for urban expressways by matching similar spatiotemporal traffic patterns

Travel time is an effective measure of roadway traffic conditions. The provision of accurate travel time information enables travelers to make smart decisions about departure time, route choice and congestion avoidance. Based on a vast amount of probe vehicle data, this study proposes a simple but efficient pattern-matching method for travel time forecasting. Unlike previous approaches that directly employ travel time as the input variable, the proposed approach resorts to matching large-scale spatiotemporal traffic patterns for multi-step travel time forecasting. Specifically, the Gray-Level Co-occurrence Matrix (GLCM) is first employed to extract spatiotemporal traffic features. The Normalized Squared Differences (NSD) between the GLCMs of current and historical datasets serve as a basis for distance measurements of similar traffic patterns. Then, a screening process with a time constraint window is implemented for the selection of the best-matched candidates. Finally, future travel times are forecasted as a negative exponential weighted combination of each candidate’s experienced travel time for a given departure. The proposed approach is tested on Ring 2, which is a 32km urban expressway in Beijing, China. The intermediate procedures of the methodology are visualized by providing an in-depth quantitative analysis on the speed pattern matching and examples of matched speed contour plots. The prediction results confirm the desirable performance of the proposed approach and its robustness and effectiveness in various traffic conditions.

Schedule Consistency for Daily Activity Chains in Integrated Activity-Based Dynamic Multimodal Network Assignment

The dynamic multimodal network assignment problem at the daily schedule level is addressed by integrating an activity-based model and a dynamic traffic assignment tool through a unified framework. The framework achieves this integration while retaining disaggregated individualized information. The problem is formulated as a fixed-point problem, and equilibrium is achieved by minimizing the gap between the expected travel time, which is used by the activity-based model to generate the travelers’ individual and household activity schedules, and their experienced travel times, simulated by the dynamic traffic assignment tool. The schedule adjustment problem for individuals and households is formulated as a linear optimization problem. Two measures—inconsistent-schedule penalty and number of households with unrealistic schedules—are defined to monitor the status of the equilibrium and convergence gap of the integrated system. To ensure convergence of the applied integration, heuristic strategies for selecting individuals for schedule adjustment and path swap are tested in a subarea network of Chicago, Illinois. Selecting individuals for schedule adjustment based on their inconsistent-schedule penalty reduces both defined measures significantly and leads to the convergence of the planned schedule and the experienced (i.e., simulated) schedule.

Multi-step prediction of experienced travel times using agent-based modeling

This paper develops an agent-based modeling approach to predict multi-step ahead experienced travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in a decision-making system. Each expert predicts the travel time for each time interval according to experiences from a historical dataset. A set of agent interactions is developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents associated with negligible weights with new agents. Consequently, the aggregation of each agent’s recommendation (predicted travel time with associated weight) provides a macroscopic level of output, namely the predicted travel time distribution. Probe vehicle data from a 95-mile freeway stretch along I-64 and I-264 are used to test different predictors. The results show that the agent-based modeling approach produces the least prediction error compared to other state-of-the-practice and state-of-the-art methods (instantaneous travel time, historical average and k-nearest neighbor), and maintains less than a 9% prediction error for trip departures up to 60min into the future for a two-hour trip. Moreover, the confidence boundaries of the predicted travel times demonstrate that the proposed approach also provides high accuracy in predicting travel time confidence intervals. Finally, the proposed approach does not require offline training thus making it easily transferable to other locations and the fast algorithm computation allows the proposed approach to be implemented in real-time applications in Traffic Management Centers.

In Pursuit of the Happy Transit Rider: Dissecting Satisfaction Using Daily Surveys and Tracking Data

This paper demonstrates the power and value of connecting satisfaction surveys from public transportation passengers to smartphone tracking data and automatic vehicle location (AVL) data. The high resolution of the smartphone location data allows travel times to be dissected into their individual components, and the connection with AVL data provides objective information on personal-level experiences of the respondents. Analyses show how these data can provide a quantitative understanding of the relationship between planned and provisioned service, and customer satisfaction. In-vehicle travel time data from 2,403 trips made by 529 unique participants could be obtained, along with origin wait time data for 779 of the trips and transfer time data for 188 trips. The addition of unreliability to the measurement of travel times, which is enabled by the highly detailed tracking data, shows that the relationship between passenger satisfaction and experienced travel times may be more nuanced than has previously been acknowledged. Ordinal logit model estimation results show a strong sensitivity of passenger satisfaction toward in-vehicle delays, and show that delays on board metro trains are perceived as more onerous than delays on board buses. This study also reveals the importance of obtaining a general measurement of satisfaction with transit service when repeated satisfaction measurements are conducted with respect to individual experiences. A baseline satisfaction level and a variable component as a function of experiences can be observed in the model results. Furthermore, the survey data include a measure of subjective well-being, which is a relatively new element in travel surveys. Insights are presented on the importance of this potential new covariate for future survey designs.

Analysis of inertial choice behavior based expected and experienced savings from a real-world route choice experiment

In the context of route choice, inertial behaviour shows that drivers make choices that are satisfactory rather than optimal. Consequently, drivers may not necessarily alter their choice when confronted with a travel time increase on the current choice or a travel time decrease of a choice alternative. As a consequence it can be assumed that driver only alter their choice when the utility difference between alternatives becomes larger than some individualsituation-specific threshold. Route choice data from a real-world driving experiment was used to study inertial behaviour and estimate inertia thresholds. The data analysis consisted of two parts. One addressing expected travel time savings relative to choice alternatives and another addressing experienced travel time savings on the current route. With regard to the former it was found that on average roughly one-fourth of the choices were inertial choices. Small travel time differences and dominant non-travel time route attributes had a positive effect on the frequency of inertial choices. Based on lost travel time, inertia thresholds up to 4.5 minutes or 30% of the average travel time were found, while thresholds up to 1.3 minutes or 13% were most common. Considerable differences between OD-pairs and routes indicated that thresholds are probabilistic and dependent on the choice set. Considering experienced savings, it was found that on average roughly half of the choices concerned inertial behaviour. Inertia thresholds up to 3.31 minutes or 37% of the average travel time were found, while thresholds up to 1.18 minutes or 11% were more common. Switching behaviour could not be explained by experienced savings as participants were much less responsive to experienced loss on the current route as opposed to expected loss relative to the choice alternative. Future research opportunities are: moving-average thresholds, inertia thresholds of different situations and driver behaviour types, asymmetry between gains and losses, impact of advanced traffic information systems, and estimation of a model that better matches the route choice data than a simple model that is based on travel time alone does.

Day-to-day travel time perception modeling using an adaptive-network-based fuzzy inference system (ANFIS)

Travel time perception and learning play a central role in the modeling of day-to-day travel choice dynamics in traffic networks and have attracted the attention of many researchers, specifically for the analysis and operation of intelligent transportation systems and travel demand management scenarios. In this paper, a fuzzy learning model is proposed to capture the mechanism by which travelers update their travel time perceptions from one day to the next, taking into account their experienced travel times. To capture travelers’ mental representations of uncertain travel time involving imprecision and uncertainty, a combined artificial neural network and fuzzy logic (neuro-fuzzy) architecture called adaptive-network-based fuzzy inference system is employed. This framework, which utilizes a set of fuzzy if–then rules, can serve as a basis for modeling the qualitative sides of travelers’ knowledge and reasoning processes. From the output of this study, the results of our laboratory-like experiment provide a good fit to the stated data of travelers’ behavior, and may reflect the fact that the neuro-fuzzy approach can be considered a promising method in learning and perception updating models. Finally, the proposed learning model is embedded in a microscopic event-based simulation framework to evaluate its credibility within a day-to-day behavior of the traffic network. The results of the simulation, which converge to the equilibrium state of the test network, are finally presented, implying that the proposed perception updating model operates properly.

Use of Data from Point Detectors and Automatic Vehicle Identification to Compare Instantaneous and Experienced Travel Times

Most traffic management centers use detector data to estimate instantaneous travel times. Interest is increasing in using automatic vehicle identification (AVI) readers to provide travel time measurements as well as in using predictive modeling of travel times. This study aimed to examine the differences between travel time estimation that was based on detector data versus those based on AVI data. In addition, the study compared instantaneous travel time estimates with experienced travel time estimates to determine the adequacy of disseminated instantaneous travel time information and, thus, the potential benefits of using predictive travel time modeling. The results showed that, for uncongested conditions, the difference between point detector- and AVI-based estimates and between instantaneous and experienced travel times was insignificant. During congested traffic conditions, the difference between estimates based on detector data and those based on AVI data (Bluetooth and electronic toll tag reader data) was about 6% to 17%. In addition, a difference of 10% to 20% existed between instantaneous and experienced travel times estimated from both the detector data and AVI data; this difference depended on the tested scenarios. The values of the differences between instantaneous and experienced travel times from both types of data sources are expected to be affected by the queue-forming and -dissipating speeds, route length, and the location of the congestion.

Day-to-day Traffic Dynamics: Laboratory-like Experiment on Route Choice and Route Switching in a Simple Network with Limited Feedback Information

An experimental analysis of day-to-day route choice dynamics in a simple three-route network is described in this paper. A laboratory-like experiment involving thirty subjects was conducted over a 50-period time span. Participants acted non- cooperatively and at each period received only feedback information on travel times of chosen routes. The results indicate that the day-to-day route choice process was characterized by a high degree of volatility, and that User Equilibrium, even though reached occasionally, did not persist as a steady state of the network. However, the detection of statistically significant trends underlying the evolution of network state descriptors suggests the possibility that equilibrium conditions might have been approached over an appropriately extended time horizon. Individual route switching frequency was found to be significantly correlated to average experienced travel time, while both variables did not exhibit any significant relationship to personal characteristics of participants. Finally, observations collected in the experiment were used to derive estimates of behavioral parameters of a deterministic process model of day-to-day route choice dynamics.

Modeling travel impedance to medical care for children with birth defects using Geographic Information Systems.

Children with birth defects may face significant geographic barriers accessing medical care and specialized services. Using a Geographic Information Systems-based approach, one-way travel time and distance to access medical care for children born with spina bifida was estimated. Using 2007 road information from the Florida Department of Transportation, we built a topological network of Florida roads. Live-born Florida infants with spina bifida during 1998 to 2007 were identified by the Florida Birth Defects Registry and linked to hospital discharge records. Maternal residence at delivery and hospitalization locations were identified during the first year of life. Of 668 infants with spina bifida, 8.1% (n = 54) could not be linked to inpatient data, resulting in 614 infants. Of those 614 infants, 99.7% (n = 612) of the maternal residential addresses at delivery were successfully geocoded. Infants with spina bifida living in rural areas in Florida experienced travel times almost twice as high compared with those living in urban areas. When aggregated at county levels, one-way network travel times exhibited statistically significant spatial autocorrelation, indicating that families living in some clusters of counties experienced substantially greater travel times compared with families living in other areas of Florida. This analysis demonstrates the usefulness of linking birth defects registry and hospital discharge data to examine geographic differences in access to medical care. Geographic Information Systems methods are important in evaluating accessibility and geographic barriers to care and could be used among children with special health care needs, including children with birth defects.

Experienced travel time prediction for congested freeways

Travel time is an important performance measure for transportation systems, and dissemination of travel time information can help travelers make reliable travel decisions such as route choice or departure time. Since the traffic data collected in real time reflects the past or current conditions on the roadway, a predictive travel time methodology should be used to obtain the information to be disseminated. However, an important part of the literature either uses instantaneous travel time assumption, and sums the travel time of roadway segments at the starting time of the trip, or uses statistical forecasting algorithms to predict the future travel time. This study benefits from the available traffic flow fundamentals (e.g. shockwave analysis and bottleneck identification), and makes use of both historical and real time traffic information to provide travel time prediction. The methodological framework of this approach sequentially includes a bottleneck identification algorithm, clustering of traffic data in traffic regimes with similar characteristics, development of stochastic congestion maps for clustered data and an online congestion search algorithm, which combines historical data analysis and real-time data to predict experienced travel times at the starting time of the trip. The experimental results based on the loop detector data on Californian freeways indicate that the proposed method provides promising travel time predictions under varying traffic conditions.

Modelling travellers' heterogeneous route choice behaviour as prospect maximizers

The use of Prospect Theory to model route choice has increased in the past decades. The main application issue is how to define the reference point, i.e., the value that travellers use as a reference to distinguish gains and losses in the experienced travel times. Moreover, the question can be asked whether all travellers have the same reference point or whether heterogeneity in their behaviour plays an important role.This paper aims to (i) provide a behavioural interpretation of the reference point, (ii) investigate the role of heterogeneity in the reference point and (iii) discuss how to take heterogeneity into account. These aspects are discussed with the aid of an empirical route choice experiment and a model specification in which travel time is the main variable. Two model frameworks are proposed, one accounting for heterogeneity and another considering no heterogeneity in travellers' behaviour, and their outcomes are compared.Results show improvements in the ability of Prospect Theory to predict route choice behaviour by accounting for heterogeneity in the reference point. This is particularly the case when the reference point reflects travelers' route preferences. Statistical analyses show the significance of accounting for heterogeneity in travellers' behaviour. Thus, we cannot reject the hypothesis that heterogeneity leads to improvements in the prediction ability of Prospect Theory.