Actual Travel Time Research Articles

Evaluating Bluetooth and Wi-Fi Sensors as a Tool for Collecting Bicycle Speed at Varying Gradients

The wide applications of mobile units with communication technology opens up new possibilities in data collection among road users. Sensors detecting Bluetooth and WiFi units have been successfully applied in collecting travel times from motorized vehicles. The same technology could potentially be applied to bicycle transport. As part of a policy for facilitating sustainable transport modes, more knowledge about bicyclists is needed.This paper presents a study answering two research questions: 1. Will the use of Bluetooth and WiFi sensors provide reliable travel time data from bicyclists? 2. How are bicycle section speeds affected by different gradients?To answer the first research question a two step test was set up to validate the equipment. Each equipment set consisted of one Bluetooth sensor and one WiFi sensor. Two sets of equipment were placed along a bicycle path with a distance of 550 meters between them. There was no adjacent road, ensuring only bicyclists and pedestrians would be registered.Firstly, a controlled test was conducted with known Bluetooth and WiFi units brought by test cyclists. The test took place nighttime to avoid disturbance from other travelers. To validate the measured travel times, actual travel times were also registered manually. The test concluded that travel times based on Bluetooth were more accurate than travel times based on WiFi. However, both sensors provided travel times that were not significantly different from the actual ones.Secondly, an open test was conducted along the same cycle path section during rush hours to find the penetration rate (i.e. the number of registered travel times divided to the total number of passing bicyclists), and to manually register all travel times to see whether the registered travel times were representative of the travel times for all passing bicyclists. The test showed that to reduce the variance in the registered data in order to calculate a valid average travel time, a minimum of 20 observations were needed. The test further showed that more than 90% of the registered travel times were detected by the WiFi unit.The second research question was addressed by selecting 7 road sections with average gradients varying from 0.5% to 9.2%, using section lengths between 500 to 1500 meters. A total of approximately 2400 travel times were registered, giving the following main findings: The average speed varied from 22.4km/h to 8.6km/h uphill according to increasing gradient. The variance is reduced by increasing gradient. When cycling downhill the average speed varies between 21.3km/h and 36.5km/h, registering the highest speed at 5.4% gradient, showing that bicyclists choose lower speeds on steep downhills. The variance seems unaffected by the gradient.Based on this study, sensors using Bluetooth and WiFi technology can be recommended as tools to collect travel times from bicycle traffic. However, some important prerequisites are needed: the sensor locations have to be carefully chosen, as do appropriate filtering of data to avoid inclusion of pedestrians and motorists in the dataset.

Calibration of the Fundamental Diagram Based on Loop and Probe Data

The fundamental diagram is a key component of traffic flow. This diagram describes equilibrium traffic states and their propagation on a traffic network. Knowledge of traffic parameters is of paramount importance to understand traffic properties and characteristics. It is also critical to calibrate the elements of dynamic traffic flow simulation models and reproduce traffic states on road networks. This paper describes the development of a method for estimating fundamental diagram parameters that combines loop data and probe data. Loop data are considered boundary conditions of the problem. The travel times between any points located in the loop can be estimated on the basis of the fundamentals of the kinematic wave theory. The optimal fundamental diagram parameters are computed so that the discrepancy between estimated travel times and the actual travel times determined with probe vehicles is minimal. The method is validated by the use of simulated error-free data. The results demonstrate the accuracy of the method when it is applied to an error-free data set. The method is then implemented with realistic data, that is, data that were aggregated and noised beforehand. The robustness of the method is demonstrated, and the results are encouraging for the development of an algorithm that calibrates fundamental diagram parameters online and automatically.

Vulnerability of road network based on generalised travel time

In this paper the vulnerability of a transportation network is defined from the perspective of the sensitivity of the transportation system against external disturbances. The generalised travel time is defined as the weighting of both the ratio of actual travel time to free travel time and the unreliability of the travel time. The reciprocal of generalised travel time taken as the utility function, the impact of external disturbances on the transportation system can be quantified. Meanwhile, the vulnerability model is established using the change rate of the utility index. The stochastic user equilibrium traffic assignment model is formulated on the assumption of minimisation of travel cost. Finally, the real road network of the city of Harbin in China is taken as an example to illustrate the applicability and efficiency of the proposed model. This approach can be used as a decision-supporting tool for identifying critical infrastructures in transportation management and planning.

Route choice behavior in a radial structured urban network: Do people choose the orbital or the route through the city center?

We use a license plate survey to study route choice through the city center of a medium-sized Dutch city, in which car drivers can basically choose between the orbital and center ring. For a sample of 1397 trips, we fitted a multinomial logit regression model. According to this model, route choice is relatively little influenced by actual travel time. This corresponds with the fact that many drivers did not choose the shortest time route. Travel distance in combination with one “route type velocity” for all orbital routes, and one route type velocity for all center routes is the most decisive factor. The route type velocity indicates how fast and attractive routes of that type are (being perceived). The results support the hypothesis that orbital routes are more attractive as these routes avoid the busy city center. This effect is however partly offset by the fact that drivers also prefer routes in the direction of their destination. These direct routes are mainly center routes. The results show the importance of considering complete routes in relation to their location and not only as a set of links and nodes when modeling route choice.

Patient preferences for timing and access to radiation therapy.

Patient preferences for radiation therapy (rt) access were investigated. Patients completing a course of rt at 6 centres received a 17-item survey that rated preferences for time of day; day of week; actual, ideal, and reasonable travel times for rt; and actual, ideal, and reasonable times between referral and first oncologic consultation. Patients receiving single-fraction rt or brachytherapy alone were excluded. Of the respondents who returned surveys (n = 1053), 54% were women, and 74% had received more than 15 rt fractions. With respect to appointment times, 88% agreed or strongly agreed that rt between 08h00 and 16h30 was preferred; 14%-15% preferred 07h30-08h00 or 16h30-17h00; 10% preferred 17h00-18h00; and 6% or fewer preferred times before 07h30 or after 18h00. A preference not to receive rt before 07h30 or after 18h00 was expressed by 30% or more of the respondents. When days of the week were considered, 18% and 11% would have preferred to receive rt on a Saturday or Sunday respectively; 52% and 55% would have preferred not to receive rt on those days. A travel time of 1 hour or less for rt was reported by 82%, but 61% felt that a travel time of 1 hour or more was reasonable. A first consultation within 2 weeks of referral was felt to be ideal or reasonable by 88% and 73% of patients respectively. An rt service designed to meet patient preferences would make most capacity available between 08h00 and 16h30 on weekdays and provide 10%-20% of rt capacity on weekends and during 07h30-08h00 and 16h30-18h00 on weekdays. Approximately 80%, but not all, of the responding patients preferred a 2-week or shorter interval between referral and first oncologic consultation.

Estimating route travel time reliability from simultaneously collected link and route vehicle probe data and roadway sensor data

Route travel time variability estimates provide performance information related to the reliability of a trip and allow for confidence bands to be placed around the mean travel time estimates. The naïve (and sometimes used) method to estimate route travel time variance (reliability) is to assume independence of link travel times and consequently sum the individual link variances along the route. In this approach, correlation between links is assumed as zero and the approach is straightforward, but assuming independence is not realistic. This paper describes a post-processing procedure for providing improved route travel time mean and variance estimates, while taking into consideration the correlation existent between individual link travel times. Using automatic vehicle identification (AVI) and inductance loop detector (ILD) data from two separate routes in Texas, a practical application of the theory established by Fu and Rilett (1998. Expected shortest paths in dynamic and stochastic traffic networks. Transportation Research Part B: Methodological, 32(7), 499–516) is used to quantify link travel time correlation. The paper also provides an investigation on the usefulness of the loess statistical method and a polynomial regression model to estimate the distributional properties of link and route travel times. Another significant finding is that route reliability estimated from link ILD speeds (extrapolated to travel times) was not correlated to actual route travel time reliability measured by simultaneously operated probe vehicles. This work is unique in that instrumented probe vehicles were operated at exactly the same times as the roadway sensor data were collected, allowing direct comparison of the travel time estimates from all empirical data sources. The research presents valuable insight on how confidence intervals may be placed on travel time mean estimates for all traffic conditions. With the increased use of travel time data sources such as smartphones, connected vehicles, and private-sector data sources, the methods presented in this paper are invaluable for effective transportation system performance monitoring of both persons and freight movement.

Travel Time Estimation Based on Spot Speed with Instantaneous and Time Slice Model

In off-line applications, travel time is the main parameter of road performance which can be the main consideration for evaluation and planning of transportation policy, and also to assess the accuracy of transportation modeling. While in on-line application travel time is main information for road users to define their travel behavior. Due to the important of travel time, therefore accurate estimation/prediction of travel time is essential. In order to fulfill it, this research analyzed the accuracy of Instantaneous and Time Slice model, and also evaluate the validity of Time mean speed and Space mean speed in mixed traffic condition. There is not much difference in travel time estimation error between models. The travel time estimation was larger than the actual travel time by floating car. It was also found that the error occurred on time mean speed are less than the space mean speed.

Information provision strategies eliminating deluded equilibrium caused by travellers’ misperception

Providing travel time information may be effective at reducing travel costs. However, this information does not always match the actual travel time that travellers will experience. Furthermore, the information is often asymmetrically provided within the network, owing to the limitations of observation devices, prediction model calibration, and uncertainty about road conditions. The purpose of this study is to investigate the effects of predictive travel time information that is asymmetrically provided to travellers. This study formulated a dynamic traffic assignment model in origin–destination (OD) pair with two parallel routes, while considering travellers’ learning processes and within-day and day-to-day dynamics. In this study, it is assumed that different information will be provided to each traveller, according to within-day traffic dynamics. Furthermore, the information is provided for only one of two possible routes, because of observation limitations. The effects of information accuracy are also discussed in this study. The results of numerical analysis indicated that information provisions possibly reduced the negative effects of deluded equilibrium state, even when the information was only provided for one of the routes. Different effects of the travel time information and its variation were illustrated according to the allocation of the bottleneck capacities of two routes.

Signal Progression Impacts on Transit Buses As Travel Time Probes

AbstractWith the deployment Automatic Vehicle Location systems to monitor transit vehicles (and other fleet vehicles), there have been several efforts in recent years that seek to leverage these data to also obtain arterial travel times for the private vehicle population. The fleet vehicle data capture the traffic conditions, but they also capture behavior unique to the fleet, e.g., servicing passengers at a bus stop. There are several strategies used in conventional practice to eliminate the biases that occur in the vicinity of the bus stop. While investigating the benefits of using a perception sensor to identify ambient traffic conditions around the bus and correct for transit operations, the present work revealed that even a perfect correction for local conditions resulted in large deviation between the travel time measured from the bus and the actual travel time experienced by the surrounding private vehicles. This research uncovered a fundamental issue affecting almost all systems that use buses as ...

Explanatory factors of distorted perceptions of travel time in tram

This study has two aims. The first is to research the factors that influence deviations in perceptions of travel time with respect to actual travel time by analyzing distorted perceptions of travel time in tram. The second aim is to find out whether these explanatory factors are different for the two segments of users according to the sign of errors in their travel time perceptions, i.e. those who overperceive and those who underperceive travel time.This work is based on an internet survey carried out on students from the University of La Laguna, Spain, in May 2009, using revealed preference data. Several linear regression models were estimated using the backward elimination method of selecting variables. The variable called “perception error rate” is proposed as a measure of distorted perceptions of in-vehicle travel time and is explained as a function of actual in-vehicle travel time and a series of other novel variables, such as the perception of other travel time components (access, waiting, egress), as well as certain characteristics of the trip and traveler. The results obtained provide evidence that some of the variables that explain the distorted perceptions of travelers are different between the overperceiving and underperceiving samples. This finding shows that estimating just one model with a total sample could lead to erroneous conclusions. Since travelers make decisions based on their perceived travel times, transport policy should focus on overperceiving travelers, and specifically on those who travel frequently. According to our results, policies should be aimed at facilitating access to stops, increasing frequency of service to reduce waiting time and decreasing the relative advantages associated with the use of private vehicles. This last point is because travelers overperceive travel time by tram more when accessing tram stops by car.

Abstract 51: Access to Primary Stroke Centers for Older Adults and Implications for Travel Times, and Mortality

Background: Regionalization of care to primary stroke centers (PSC) may improve outcomes for stroke patients. We evaluated the current access of Medicare stroke patients to PSC, and its potential impact on mortality. Methods: We performed a retrospective cohort study of a 100% sample of Medicare fee-for-service claims data for patients admitted with stroke in 2008-2009, with one-year follow-up through 2010. Population weighted centroids were created, helicopter pad locations were identified, and driving distances were calculated based on real road network data. Driving and flying speeds, dispatch, scene, and pre-hospital times were estimated using validated models, adjusted for population density. The association of 30-day mortality with travel times, and treatment at a PSC was investigated using multivariable regression models. Results: During the study period, 510,822 patients (mean age 79.6 years, 59.7% females) had a stroke. There was significant regional variation in our cohort, (Figure) with 8.6% of stroke patients having ground access to a PSC within 30 minutes, 14.9% from 30 to 45 minutes, 11.1% from 45 to 60 minutes, 43.9% from 1 to 4.5 hours, and 21.5% over 4.5 hours. The latter group could be limited to 0.1% of stroke patients, if existing helicopter services were used optimally. 164,485 (32.2%) patients received treatment in a PSC, and had modestly decreased mortality (OR, 0.97; 95% CI, 0.95-0.99). For this group, actual travel time to the PSC was not associated with mortality for patients within one hour of the PSC. On the contrary, travel times from 1 to 4.5 hours (OR, 1.15; 95% CI, 1.08-1.22), and over 4.5 hours (OR, 1.41; 95% CI, 1.29-1.54) were associated with increased mortality. Conclusions: There is significant regional variation in access to PSC for elderly stroke patients, with a potential impact on outcomes. Optimal use of helicopter services may address these disparities. Funding: NIH (P01-AG19783, and U01-AG046830-01).

An evaluation of section control based on floating car data

Floating Car Data (FCD) consists of positional information from moving vehicles and is ideally suited to monitor traffic conditions in large road networks. Given a large mobile fleet equipped with the FCD-system, FCD allows to determine actual travel times on road segments/links and determine traffic state. Based on this link-specific information, we analyse the effectiveness of section control, also called average speed control, on 2 Belgian highways. Section control systems measure the travel time of vehicles between different positions to determine the speed limit compliance. Results show that the section control systems reduce speed limit violations over the entire length of the system, contrary to the local effect of fixed traffic cameras. Section control systems also reduce speed differences between vehicles in the same section as the speed dispersion is much smaller compared to similar, uncontrolled parts of the highway.

Information Provision Strategies Eliminating Deluded Equilibrium Caused by Travellers’ Misperception

Providing travel time information may be effective at reducing travel costs. However, this information does not always match the actual travel time that travellers will experience. Furthermore, the information is often asymmetrically provided within the network,owing to the limitations of observation devices, prediction model calibration, and uncertainty about road conditions.The purpose of this study is to investigate the effects of predictive travel time information that is asymmetrically provided to travellers. This study formulated a dynamic traffic assignment model in origin-destination (OD) pair with two parallel routes, while considering travellers’ learning processes and within-day and day-to-day dynamics.In this study,it is assumed that different information will be provided to each traveller, according to within-day traffic dynamics. Furthermore, the information is provided for only one of two possible routes,because of observation limitations. The effects of information accuracy are also discussed in this study.The results of numerical analysis indicated that information provisions possibly reduced the negative effects of deluded equilibrium state, even when the information was only provided for one of the routes. Different effects of the travel time information and its variation were illustrated according to the allocation of the bottleneck capacities of two routes.

An Optimization Method of Time Window Based on Travel Time and Reliability

The dynamic change of urban road travel time was analyzed using video image detector data, and it showed cyclic variation, so the signal cycle length at the upstream intersection was conducted as the basic unit of time window; there was some evidence of bimodality in the actual travel time distributions; therefore, the fitting parameters of the travel time bimodal distribution were estimated using the EM algorithm. Then the weighted average value of the two means was indicated as the travel time estimation value, and the Modified Buffer Time Index (MBIT) was expressed as travel time variability; based on the characteristics of travel time change and MBIT along with different time windows, the time window was optimized dynamically for minimum MBIT, requiring that the travel time change be lower than the threshold value and traffic incidents can be detected real time; finally, travel times on Shandong Road in Qingdao were estimated every 10 s, 120 s, optimal time windows, and 480 s and the comparisons demonstrated that travel time estimation in optimal time windows can exactly and steadily reflect the real-time traffic. It verifies the effectiveness of the optimization method.

Consequences of mixing assumptions for time‐variable travel time distributions

AbstractThe current generation of catchment travel time distribution (TTD) research, integrating nearly three decades of work since publication of Water's Journey from Rain to Stream, seeks to represent the full distribution in catchment travel times and its temporal variability. Here, we compare conceptualizations of increasing complexity with regards to mixing of water storages and evaluate how these assumptions influence time‐variable TTD estimates for two catchments with contrasting climates: the Gårdsjön catchment in Sweden and the Marshall Gulch catchment in Arizona, USA. Our results highlight that, as long as catchment TTDs cannot be measured directly but need to be inferred from input‐output signals of catchments, the inferred catchment TTDs depend strongly on the underlying assumptions of mixing within a catchment. Furthermore, we found that the conceptualization of the evapotranspiration flux strongly influences the inferred travel times of stream discharge. For the wet and forested Gårdsjön catchment in Sweden, we inferred that evapotranspiration most likely resembles a completely mixed sample of the water stored in the catchment; however, for the drier Marshall Gulch catchment in Arizona, evapotranspiration predominantly contained the younger water stored in the catchment. For the Marshall Gulch catchment, this higher probability for young water in evapotranspiration resulted in older water in the stream compared to travel times inferred with assumptions of complete mixing. New observations that focus on the TTD of the evapotranspiration flux and the actual travel time of water through a catchment are necessary to improve identification of mixing and consequently travel times of stream water. Copyright © 2014 John Wiley & Sons, Ltd.

Valuing environmental quality in actual travel time savings – The Haningeleden road project in Stockholm

Effects on the environment are rarely valued in planning infrastructure for different modes of transport. This can detract from optimal planning and fair competition, which might be better served by valuing environmental quality changes through actual travel time savings. New transport infrastructure often creates both positive and negative effects for the same groups of users, although to different degrees for different individuals. This paper illustrates a proposed methodology to investigate the effect of a planned transport infrastructure investment through a survey of potential users of the Haningeleden road project in Stockholm. Postal questionnaires were sent to a random sample of 2400 persons living in the affected area in May and June 2013, including detailed questions about their travel patterns and visiting frequencies, and whether, after reading a description of the proposed change, they would be in favour of the proposed change. The negative encroachment costs and the positive relief from traffic in other areas were estimated in travel time savings.

Travel time modeling for bus transport system in Bangalore city

The aim of this work is to model travel time of buses in Bangalore city. Travel time estimation and prediction is inevitable for planning and operation of bus transport system. Moreover, accurate travel time prediction is an important requirement in most of the Intelligent Transport System (ITS) applications. Previous approaches on travel time prediction have been mostly done in “lane disciplined” and “homogeneous traffic conditions.” In this work, an attempt is made to predict travel time of public transport buses using Kalman filtering technique in Indian conditions where the traffic is heterogeneous and not lane disciplined. The additional challenge faced in the Indian conditions is the unavailability and/or ineffectiveness of automated data collection devices such as loop detectors. Data collection was done in Bangalore, India for different times of a day and also for different days of a week for understanding the travel time variation on different days. A prediction model was developed using Kalman filtering technique. The validation of the results shows good comparability of modeled values with the actual travel time values.

Perception bias in route choice

Travel time is probably one of the most studied attributes in route choice. Recently, perception of travel time received more attention as several studies have shown its importance in explaining route choice behavior. In particular, travel time estimates by travelers appear to be biased against non- chosen options even if these are faster. In this paper, the authors study travel time perception and route choice of routes with different degrees of road hierarchy and directness. In the Dutch city of Enschede, respondents were asked to choose a route and provide their estimated travel times for both the preferred and alternative routes. These travel times were then compared with actual travel times. Results from previous studies were confirmed and expanded. The shortest time route was chosen in 41% of the cases while the perceived shortest time route was chosen by almost 80% of the respondents. Respondents overestimated travel time in general but overestimated the travel time of non-chosen routes more than the travel time of chosen routes. Perception of travel time depends on road hierarchy and route directness, as more direct routes and routes higher up in the hierarchy were perceived as being relatively fast. In addition, there is evidence that these attributes also influence route choice independently of perceived travel time. Finally, travel time perceptions appear to be most strongly biased against non-chosen options when respondents were familiar with the route or indicated a clear preference for the chosen routes. This result indicates that behavior will be more difficult to change for the regular travelers.

A Day-to-day Route Choice Model based on Travellers’ Behavioural Characteristics

This paper proposes a day-to-day route choice model based on travellers’ behavioural characteristics. The model is able to reflect travellers’ route choice in one day and describes the diversity of route choice behaviours. First, characteristics of the day- to-day route choice and travellers’ bounded rationality are analysed. A loop architecture based on the evolution of travellers’ daily choices is presented; deliberate route choice and habitual route choice in the architecture are discussed. A model is then proposed based on the theoretical analysis. In this model, travellers’ behavioural characteristics in the day-to-day route choice are considered; i.e., forgetting previous travel times, risk attitude, habit effect, traveller's amount of attention on the route choice being made, perception of actual travel time, etc. Results of a numerical experiment show that the tendencies of the simulated travellers’ choices are reasonable and similar with that in reality, and that the model is able to reproduce travellers’ route choice with more behavioural characteristics.

Reliability of Bluetooth Technology for Travel Time Estimation

A unique Bluetooth-enabled device may be detected several times or not at all when it passes a sensor location. This depends mainly on the strength and speed of a transmitting device, discovery procedure, location of the device relative to the Bluetooth sensor, the Bluetooth sensor's ping cycle (0.1 s), the size and shape of the sensor's detection zone, and the time span for which the Bluetooth-enabled device is within the detection zone. The influences of size of Bluetooth sensor detection zones and Bluetooth discovery procedure on multiple detection events have been mentioned in previous research. However, their corresponding impacts on accuracy and reliability of estimated travel time have not been evaluated. In this study, a controlled field experiment is conducted to collect both Bluetooth and global positioning system (GPS) data for 1000 trips to be used as the basis for evaluation. Data obtained by GPS logger are used to calculate actual travel time, referred to as ground truth, and to geo-code the Bluetooth detection events. In this setting, reliability is defined as the percentage of devices captured per trip during the experiment. It is found that, on average, Bluetooth-enabled devices will be detected 80% of the time while passing a sensor location. The impact of location ambiguity caused by the size of the detection zone is evaluated using geo-coded Bluetooth data. Results show that more than 80% of the detection events are recorded within the range of 100 m from the sensor center line. It is also shown that short-range antennas detect Bluetooth-enabled devices in a location closer to the sensor, thus providing a more accurate travel time estimate. However, the smaller the size of the detection zone, the lower is the penetration rate, which could itself influence the accuracy of estimates. Therefore, there has to be a trade-off between acceptable level of location ambiguity and penetration rate for configuration and coverage of the antennas.