Arterial roadway travel time distribution estimation and vehicle movement classification using a modified Gaussian Mixture Model

Understanding the nature of human movement is important in many fields, such as transportation management, urban planning, and epidemiology. In this study, we focus on travelers who are grouped by their travel modes in the Tokyo Metropolitan Area. Notably, for each group of travelers, the distribution of daily travel time follows a lognormal distribution. For any travel mode, the distributions of normalized travel time for different years (1968–2008) and distances to the city center converge to a curve, although the average travel time and share of travel modes vary. By employing the concept of utility, we developed a disaggregate model wherein an individual maximizes utility from traveling (=benefit - cost) by modifying daily travel time. This model theoretically explained the observed distributions of travel time. The model was further used to demonstrate travel mode choices. The model parameters were estimated using the distribution of travel time, travel mode share, and manageable assumptions. These results provided interesting interpretations of the perceptual utility of the daily travel time of a population. Moreover, the obtained curves of utility versus time are similar to the shapes of the overall benefits assumed in previous studies. Overall benefit is a concept that has been developed and examined since the 1980s, and our study provides a mathematical foundation for this idea.

Riverbank filtration (RBF) is a surface water treatment technology that uses the aquifer adjacent to the river as a natural filter. A production well in the aquifer near the river draws water from both the aquifer and the river. As river water travels through the aquifer to the well, contaminants in the water undergo biologic and chemical reactions while traveling through the subsurface materials, leading to higher quality water at the well as compared with the river. A key parameter in siting the RBF well is the travel time of the contaminants from the river to the well, which must be sufficiently long to allow adequate time for biologic and chemical reactions to occur. Because the well induces flow into the aquifer from all along the river channel, multiple travel paths exist between the river and the well, each with a different contaminant travel time. The water produced at the well contains a collection of contaminant particles from these multiple travel paths and can be characterized by a distribution of travel times. The travel time of the contaminant depends on the groundwater velocity along the flow path, the sorption properties of the contaminant and the aquifer matrix, and the chemical reactions that occur during transport. In this work, we demonstrate the use of an adjoint-based modeling approach to characterize the distribution of travel times at the RBF well. In adjoint modeling, transport is modeled backward in space and time, from the point of view of the RBF well. The output of an adjoint simulation is the distribution of solute travel times at the RBF well at any time of interest. We consider different sorption models and sorption parameters, and we consider different chemical reactions and reaction rates. We simulate the travel time distributions using either MT3DMS or RT3D, depending on the type of chemical reaction, using a flow field generated using MODFLOW.

Travel time is inherently uncertain in urban networks due to volatile traffic flows, signal controls, bus stops and disturbances from pedestrians. An effective way to characterize such uncertainty is by estimating Travel Time Distribution (TTD). However, conventional TTD models are lack of considering the interactions between turning movements within the link. Whereas the phenomenon of the Channelization Section Spillover (CSS) is very common, leading to strong interactions between turning movements. In this study, by incorporating the correlation of turning movements in the TTD model, that is, considering CSS, copula-based link-level and path-level TTD models are built. First, based on the empirical data, the correlations of turning movements are analyzed, and then the applicability of the various copula models are examined. The marginal distribution of each turning movement is described using parametric and non-parametric regression analysis, respectively. Then, the best-fitting copula is determined based on correlation parameters and the goodness-of-fit tests. As a case study, the chosen model is applied in estimating link-level and path-level TTD in an arterial road in Hangzhou, China, and compared with the model that did not consider the CSS before. Both results indicate that the copula-based approach can precisely capture the positively correlated relationship between turning movements during peak hours. Furthermore, higher TTD estimation accuracy demonstrates the significance to consider CSS, particularly in peak hours.

Travel time distribution studies are fundamental for supporting transportation system reliability studies, particularly for urban road networks. However, such studies are generally based on travel time data sets with limited sample sizes, which provide inconsistent findings. In this paper, a large amount of travel time data collected from the emerging radio frequency identification (RFID) technique are used to conduct empirical investigations and estimations of travel time distributions, and three major findings are determined. First, travel time data are shown to have a complex statistical structure: the travel time distribution is in general peaky, multi-modal, and skewed to the right, which cross validates findings shown in previous publications. Second, unimodal distribution models are shown to be unable to capture the complex statistical dynamics embedded in the travel time data; therefore, a multistate distribution model is more appropriate for modeling travel time distributions. In this respect, a three-component gaussian mixture model (GMM) is tested and results consistently outperform those of unimodal distribution models. Finally, the aggregation time interval is shown to have a trivial effect on the shape of travel time distributions: the travel time distribution is stable under different aggregation time intervals. Future work is recommended to investigate further travel time variabilities and travel time distribution estimations.

Travel time reliability (TTR) is widely used to evaluate transportation system performance. Adverse weather condition is an important factor for affecting TTR, which can cause traffic congestions and crashes. Considering the traffic characteristics under different traffic conditions, it is necessary to explore the impact of adverse weather on TTR under different conditions. This study conducted an empirical travel time analysis using traffic data and weather data collected on Yanan corridor in Shanghai. The travel time distributions were analysed under different roadway types, weather, and time of day. Four typical scenarios (i.e., peak hours and off-peak hours on elevated expressway, peak hours and off-peak hours on arterial road) were considered in the TTR analysis. Four measures were calculated to evaluate the impact of adverse weather on TTR. The results indicated that the lognormal distribution is preferred for describing the travel time data. Compared with off-peak hours, the impact of adverse weather is more significant for peak hours. The travel time variability, buffer time index, misery index, and frequency of congestion increased by an average of 29%, 19%, 22%, and 63%, respectively, under the adverse weather condition. The findings in this study are useful for transportation management agencies to design traffic control strategies when adverse weather occurs.

A novel generative adversarial network for estimation of trip travel time distribution with trajectory data

As they involve many interacting agents behaving in numerous ways that are extremely difficult to predict, urban transportation systems are complex in nature. The development of intermodal passenger transportation solutions to address the mobility issues constitutes a major thrust area of urban transport policies. But, to offer citizens comprehensive seamless mobility, intermodal transportation system management (ITSM) requires the integration of two major components. The traffic regulation support system, to help the operator responsible for the regulation tasks: coordination of timetables, synchronising arrival and departure times between the different transportation modes, and the traveller information system, giving customers access to information and using a comprehensive set of information tools. In this paper, a generic model of a transport management system, integrating these two components is proposed. This generic model is then used to elaborate a traffic regulation system in the case of a bimodal transportation system (tram-bus). The traffic regulation support system, based on the decision model of an operator, and the traveler information system are described.

Valuing travel time variability: Characteristics of the travel time distribution on an urban road

Travel time is a basic measure based on which intelligent transportation systems such as traveler information systems, traffic management systems, public transportation systems are developed. Although many methodologies have been proposed, they have not yet adequately solved many challenges associated with travel time, in particular, travel time estimation for all links in a large and dynamic urban traffic network is still an open problem that needs addressing. Typically focus is placed on major roads such as motorways and main city arteries but there is an increasing need to know accurate travel times for minor urban roads. Such information is crucial for tackling air quality problems, accommodate the growing number of cars and provide accurate information for routing. This study aims to address the aforementioned challenges by introducing a methodology, namely Similar Model Searching (SMS), to estimate travel times by using historical sparse travel time data. The SMS learns the temporal and spatial relationship between the travel time of adjacent links and utilize labeled data of similar models in order to improve its overall performance. The effectiveness of the proposed method is evaluated on a section of Leicestershire traffic network in the UK. The obtained results show that SMS efficiently estimates travel time of target links using models of adjacent traffic links.

A copula-based approach for estimating the travel time reliability of urban arterial

Scheduling preferences and the value of travel time information

Analyzing travel time distribution based on different travel time reliability patterns using probe vehicle data

Route travel time information is essential for better travel planning, traffic guidance and congestion avoidance. An available way of representing such information is the estimation of travel time distribution (TTD). However, most methods both in literature and practice prefer to estimate route mean travel times rather than TTDs, especially with less consideration about the spatiotemporal correlations between adjacent links. This study develops a framework of estimating urban route TTD based on Markov chain approach and pair-copula construction with emphasis on capturing the dependence in time and space. The proposed method is validated with Radio Frequency Identification Data collected from an urban arterial in Nanjing, China. The results indicate that the proposed method can dynamically capture the positively correlated, negatively correlated, and uncorrelated relationships between adjacent link travel times. Moreover, the performance of the proposed method produces the least deviation from the route empirical distributions, compared to the considered competing methods.

Time-stamped, media access control addresses, acquired from Bluetooth-enabled devices to collect travel time data, have received significant attention in the past few years. Research has focused mainly on the application of Bluetooth technology to obtain travel time data on freely flowing roads. A few studies have addressed the use of Bluetooth data collection systems on arterial roads, particularly with respect to travel times between signalized intersections, with questionable accuracy. The objective of this research was to develop a methodology to collect accurate and precise travel time data between signalized intersections, with the use of a data collection system that had a Bluetooth basis. The developed methodology utilized received signal strength indicator (RSSI) data to improve signifi-cantly the accuracy of intersection-to-intersection travel time samples. Test results on a busy arterial road showed that the travel time samples generated with the RSSI method were significantly better (i.e., had less error) than the travel time samples calculated with other methods reported in the literature. The proposed RSSI travel time data collection method could be implemented with any wireless technology that provided a unique identification number to distinguish between different mobile devices and an associated signal strength measurement during the wireless communication process.

Basic reliability issues are addressed in this study, which is not concerned with average travel times, but rather ways of describing travel times that reflect the uncertainty in the amount of time required to travel between two points. Some of the uncertainty is systematic, such as the normal ebb and flow of traffic within the course of a work day or season of the year. Congestion associated with this systematic uncertainty is called recurrent. Congestion due to unpredictable or unexpected events is called nonrecurrent. If every travel time observed over a highway section for a year is plotted, a distribution of travel time is obtained. This plotted distribution is the picture of travel time variability. Such distributions are the focus of this research, especially the degree to which recurring and nonrecurring congestion influence the nature of the distribution. This research shows how to derive performance measures from such distributions and recommends a set for use by managers, planners, and systems operators. The research reexamines the composition of the congestion puzzle in terms of the fractions attributable to recurrent and various sources of nonrecurrent congestion. The project team used before-and-after studies to determine the effectiveness of different types of actions, both operational and capacity improvements, in improving reliability. This study also examined the effect of the downturn of the economy on travel time reliability. Finally, this research resulted in two types of models that can be used to estimate or predict travel time reliability. These models have broad applicability to planning, programming, and systems management and operations.