Comparative Analysis of Transportation Network Design Problem under Stochastic Capacity

Exploring travel time distribution and variability patterns is essential for reliable route choices and sophisticated traffic management and control. State-of-the-art studies tend to treat different types of roads equally, which fails to provide more detailed analysis of travel time characteristics for each specific road type. In this study, based on a vast amount of probe vehicle data, 200 links inside the Third Ring Road of Beijing, China, were investigated. Four types of roads were covered including urban expressways, auxiliary roads of urban expressways, major roads, and secondary roads. The day-of-week distributions of unit distance travel time were first analyzed. Kolmogorov-Smirnov test, Anderson-Darling test, and chi-squared test were employed to test the goodness-of-fit of different distributions and the results showed lognormal distribution was best-fitted for different time periods and road types compared with normal, gamma, and Weibull distribution. In addition, four reliability measures, that is, unit distance travel time, coefficient of variation, buffer time index, and punctuality rate, were used to explore the day-of-week travel time variability patterns. The results indicated that urban expressways, auxiliary roads of urban expressways, and major roads have regular and distinct morning and afternoon peaks on weekdays. It is noteworthy that in daytime the travel times on auxiliary roads of urban expressways and major roads share similar variability patterns and appear relatively stable and reliable, while urban expressways have most reliable travel times at night. The results of analysis help enable a better understanding of the volatile travel time characteristics of each road type in urban network.

This paper aims to provide a state-of-the-art review of the transport network design problem (NDP) under uncertainty and to present some new developments on a bi-objective-reliable NDP (BORNDP) model that explicitly optimizes the capacity reliability and travel time reliability under demand uncertainty. Both are useful performance measures that can describe the supply-side reliability and demand-side reliability of a road network. A simulation-based multi-objective genetic algorithm solution procedure, which consists of a traffic assignment algorithm, a genetic algorithm, a Pareto filter, and a Monte-Carlo simulation, is developed to solve the proposed BORNDP model. A numerical example based on the capacity enhancement problem is presented to demonstrate the tradeoff between capacity reliability and travel time reliability in the NDP.

Dynamic accessibility: Incorporating day-to-day travel time reliability into accessibility measurement

The impact of work zones on mean travel time and travel time reliability has gained attention as agencies focus on performance measurement. Travel time reliability in work zones often has been difficult for agencies to quantify because of the time and expense required to collect travel time data. Several private-sector companies have begun to sell travel time data; this availability has created an opportunity to examine cost-effectively work zone impacts on travel time reliability on a broader basis. The Virginia Department of Transportation recently acquired probe vehicle-based travel time data for 2010 from a private-sector data provider. These data were used to calculate measures of travel time reliability at 15 work zones and to examine factors that affected travel time reliability. Travel time reliability was quantified with 95th percentile travel time, a buffer index, and a planning time index. The work zones experienced a statistically significant degradation across all measures of travel time reliability as compared with baseline conditions. Work zone mean buffer index, planning time index, and 95th percentile travel time rates were higher by 48%, 18%, and 16%, respectively. Although lane closures occurred during off-peak periods, work zones that involved lane closures experienced increases in their mean buffer index, planning time index, and 95th percentile travel time with rates of 67%, 23%, and 22%, respectively. Annual average daily traffic per lane and the number of access points per mile were found to have the most obvious relationships with declines in reliability at work zones.

Modeling travel time reliability requires characterizing travel time distributions. Two key statistics commonly used to describe a distribution are the mean deviation and the standard deviation, with one depicting the central tendency and the other describing the dispersion. Although the mean travel time is easier to measure and predict, the corresponding standard deviation is usually hard to obtain because of the insufficiency of individual trip data. Building on seminal insight that goes back to Herman and Prigogine's kinetic theory, this study explores a robust characterization of travel time variability that provides for a near-linear relation between the standard deviation of travel time per unit distance and the corresponding mean value. Simulation-generated vehicle trajectory data from three road networks are used to explore this relationship. On the basis of multiscale and multilevel analysis, large amounts of data show that these two quantities are highly positively correlated; that is, the dispersion of the distribution of individual travel time per unit distance increases with increasing value of the mean travel time per unit distance. Furthermore, regression models and statistical testing indicate that this relation is linear or near-linear. The relation is also validated by Global Positioning System probe data from the Seattle, Washington, area. This relation provides a robust basis for predicting the standard deviation per unit distance when the mean value is known and thus for characterizing the reliability of travel in a network in strategic and operational studies.

Evaluating the impact of public transport travel time inaccuracy and variability on socio-spatial inequalities in accessibility

Travel time variability (TTV) is a crucial indicator of transportation network performance, assessing travel time reliability and delays. This study investigates TTV metrics within the context of shared mobility using probe data from transportation network companies (TNCs) in Chicago, Los Angeles, and Dallas–Fort Worth. Eight reliability metrics are analyzed and compared for each origin–destination (OD) pair in the network, including standard deviation (SD), the Planning Time Index (PTI), the Travel Time Index (TTI), the Buffer Index (BI), On-time Measures PR (alpha), and the Misery Index (MI), to evaluate their effectiveness in clustering OD pairs using K-means clustering. The findings confirm that SD, PTI, and MI are particularly effective in measuring travel time reliability and clustering within urban systems. This study identifies the most unbalanced supply–demand OD pairs/regions in each city, noting that low/medium-SD clusters around metropolitan airports indicate stable travel times even in high-demand zones, while high-SD clusters in downtown areas reveal significant traffic demands and unreliability. These patterns become more pronounced in study areas with multiple city centers. This study highlights the need for targeted strategies to enhance travel time reliability, particularly in regions like Dallas–Fort Worth, where public transportation alternatives are limited.

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.

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

Capturing Value of Reliability through Road Pricing in Congested Traffic under Uncertainty

Variation in Patients' Travel Times among Imaging Examination Types at a Large Academic Health System

Fosgerau's travel time reliability ratio and the Burr distribution

To study the relationship of different reliability indices for road network under stochastic demand, an evaluation method based on travel time reliability (TTR) was set up for level of service (Los) reliability. Assuming that travelers acquire the variation of the route travel time based on past experiences and factor such variability into their route choice considerations in the form of travel time budget, which was defined as the minimal travel time that can accommodate a specified travel time reliability and can be derived according to the probability characteristics of log-normal distribution. Accordingly, Los reliability evaluation models of link and road network were proposed based on the link flow distribution, which can be determined by a travel time budget-based variational inequality (VI) network equilibrium model and a route-based solution algorithm. Some numerical studies on a small road network were presented to validate the proposed method and algorithm.

This paper researches on the problem of searching for the reliable path for transportation networks. The reliable path finding problem is concerned with searching for the path with the maximum value of connectivity reliability. In view of the literature on travel time variations, the relationship between the mean travel time and its connectivity reliability is adopted in the proposed model. In order to simplify the difficulties of the solution, this paper takes the travel time and its connectivity reliability equal to a 0–1 knapsack problem, then defines a new conception of greedy transform which is given an efficient algorithm, after combining the new greedy transform method with Genetic Algorithm, obtains Hybrid Genetic Algorithm (HGA). This algorithm (HGA) can be considered as an efficient algorithm to solve the reliable path finding problem and some other knapsack problems. Numerical results are presented to demonstrate the applications of the proposed algorithm.

A Practical Method to Estimate the Benefits of Improved Network Reliability: An Application to Departing Air Passengers