Xuegang Ban

Delay Pattern Estimation for Signalized Intersections Using Sampled Travel Times

Intersection delays are the major contributing factor to arterial delays. Methods to estimate intersection delay patterns by using measured travel times are studied. The delay patterns provide a way to estimate the delay for any vehicle arriving at the intersection at any time, which is useful for providing time-dependent intersection delay information to the driving public. The model requires sampled travel times between two consecutive locations on arterial streets, one upstream and the other downstream of a signalized intersection, without the need to know signal timing or traffic flow information. Signal phases can actually be estimated from the delay patterns, which is a unique feature of the proposed method in this paper. The proposed model is based on two observations regarding delays for signalized intersections: (a) delay can be approximately represented by piecewise linear curves due to the characteristics of queue forming and discharging and (b) there is a nontrivial increase in delay after the start of the red time that enables detection of the start of a cycle. A least-squares–based algorithm is developed to match measured delays in each cycle by using piecewise linear curves. The proposed model and algorithm are tested by using field experiment data with reasonable results.

Freight Generation, Freight Trip Generation, and Perils of Using Constant Trip Rates

Several findings call into question current practices. The chief conclusion is that the accuracy of freight generation (FG) and freight trip generation (FTG) models depends on the consistency between the models structure and actual FG-FTG patterns, the degree of internal heterogeneity of the economic and land use aggregation used to estimate the model, and the appropriateness of the spatial aggregation procedure used to obtain the desired FG-FTG estimates. Relative to model structure, the paper establishes strong reasons to treat FG and FTG as separate concepts, because the latter is the output of logistic decisions, whereas the former is determined by the economics of production and consumption. The connection between business size variables–for example, employment–and FG is relatively strong because they are economic input factors, whereas the one with FTG is weaker because inventory and transportation costs come into play. Thus it is generally not correct to assume proportionality between FTG and business size or to assume that using constant FTG rates could be problematic. For instance, only 18% of the industry sectors in New York City exhibit constant FTG rates per employee. For economic and land use aggregation, the finer the level of detail the better, as independent variables have a better chance to explain FG-FTG. In the case of spatial aggregation, the correct aggregation procedure depends on the underlying disaggregate model. For a FG-FTG model to work well, both economic and land use and spatial aggregations must be appropriate.

Optimal Sensor Placement for Freeway Travel Time Estimation

This article presents a modeling framework and a polynomial solution algorithm for determining optimal locations of point detectors used to compute freeway travel times. First, an objective function is introduced to minimize the deviation of estimated and actual travel times of all individual sub-segments of a freeway route. By discretizing the problem in both time and space, we formulate it as a dynamic programming model, which can be solved via a shortest path search in an acyclic graph. Numerical examples are provided to illustrate the model and algorithm using microscopic traffic simulation and GPS data from the Mobile Centuryexperiment recently conducted by the University of California, Berkeley, Nokia and California Department of Transportation (Caltrans).

Bottleneck Identification and Calibration for Corridor Management Planning

Corridor mobility improvements require a new approach to corridor management planning and operations. Recent investigations are aimed at improving the safety and efficiency of existing transportation systems by integrating state-of-the-art operational analysis (such as microsimulation) into more traditional corridor planning. One of the important elements in developing corridor management improvements is better bottleneck analysis. Such analyses play a crucial role in corridor management planning for both performance assessment and simulation model calibration. New approaches are proposed for bottleneck identification and calibration in simulation. Identification is conducted with percentile speeds based on data from multiple days. It turns out that this method is more appropriate for urban congested freeways than use of single-day data. The algorithm for bottleneck calibration represents the first attempt to rigorously calibrate bottlenecks in microsimulation. It is a three-step process–including visual assessment, bottleneck area matching, and detailed speed calibration–aiming to calibrate bottlenecks in three levels of detail. With the I-880 corridor network in the San Francisco Bay Area of California, it has been shown that the identification method can adequately identify corridor bottlenecks; the calibration procedure complements and improves the current practice of simulation calibration.

Signal Timing Estimation Using Sample Intersection Travel Times

Signal timing information is important in signal operations and signal/arterial performance measurement. Such information, however, may not be available for wide areas. This imposes difficulty, particularly for real-time signal/arterial performance measurement and traffic information provisions that have received much attention recently. We study, in this paper, the possibility of using intersection travel times, i.e., those collected between upstream and downstream locations of an intersection, to estimate signal timing parameters. The method contains three steps: (1) cycle breaking that determines whether a new cycle starts; (2) exact cycle boundary detection that determines when exactly a cycle starts or ends; and (3) effective red (or green) time estimation that estimates the actual duration of the red (or green) time. The proposed method is a combination of traffic flow theory and learning/estimation algorithms and can be used to estimate the cycle-by-cycle signal timing parameters for a specific movement of a signal. The method is tested using data from microscopic simulation, field experiments, and next-generation simulation with promising results.

Analytical Dynamic Traffic Assignment Model with Probabilistic Travel Times and Perceptions

Dynamic traffic assignment (DTA) has been a topic of substantial research during the past decade. Although DTA is gradually maturing, many aspects still need improvement, especially regarding its formulation and solution capabilities under the transportation environment affected by advanced transportation management and information systems. It is necessary to develop a set of DTA models to acknowledge the fact that the traffic network itself is probabilistic and uncertain, and different classes of travelers respond differently under an uncertain environment given different levels of traffic information. The aim of this research is to advance the state of the art in DTA modeling in the sense that the proposed model captures the travelers’ decision making among discrete choices in a probabilistic and uncertain environment, in which both probabilistic travel times and random perception errors that are specific to individual travelers are considered. Travelers’ route choices are assumed to be made with the objective of minimizing perceived disutilities at each time. These perceived disutilities depend on the distribution of the variable route travel times, the distribution of individual perception errors, and the individual traveler’s risk-taking nature at each time instant. The integrated DTA model is formulated through a variational inequality approach. Subsequently, the solution algorithm for the formulation is discussed, and experimental results are given to verify the correctness of solutions obtained.

On the comparative performance of urban delivery vehicle classes

As it is widely known, a large truck generates more pollution and congestion than a small truck, which is an obvious consequence of its larger size. However, it is frequently overlooked that they have very different cargo productivities, and that large trucks require less trips to transport the same amount of cargo than small trucks. As a result, large trucks could produce less social costs than small trucks, as long as they generate proportionally less traffic. The objective is to identify the conditions under which large trucks are the most beneficial in terms of social costs. To gain insight into the nature of these tradeoffs, a mathematical formulation was developed to express social costs as a function of a multimodal vector of traffic flows. The key insight gained is that large trucks could indeed be better in terms of social costs if they replace a sufficient number of small trucks.

Decomposition Scheme for Continuous Network Design Problem with Asymmetric User Equilibria

The continuous network design problem is formulated as a mathematical program with complementarity constraints (MPCC) and a Gauss-Seidel decomposition scheme is presented for the solution of the MPCC model. The model has an upper level as a nonlinear programming problem and the lower level as a nonlinear complementarity problem. With the application of the complementarity slackness condition of the lower-level problem, the original bilevel formulation can be converted into a single-level nonlinear programming problem. To solve the single-level problem, a decomposition scheme that can resolve the possible dimensionality problem (i.e., a large number of defining variables) is developed. The decomposition scheme is tested, and promising results are shown for well-known test problems.

Risk Averse Second Best Toll Pricing

Existing second best toll pricing (SBTP) models determine optimal tolls of a subset of links in a transportation network by minimizing certain system objective, while the traffic flow pattern is assumed to follow user equilibrium (UE). We show in this paper that such toll design approach is risk prone, which tries to optimize for the best-case scenario, if the UE problem has multiple solutions. Accordingly, we propose a risk averse SBTP approach aiming to optimize for the worst-case scenario, which can be formulated as a min-max problem. We establish a general solution existence condition for the risk averse model and discuss in detail that such a condition may not be always satisfied in reality. In case a solution does not exist, it is possible to replace the exact UE solution set by a set of approximate solutions. This replacement guarantees the solution existence of the risk averse model. We then develop a scheme such that the solution set of an affine UE can be explicitly expressed. Using this explicit representation, an improved simplex method can be adopted to solve the risk averse SBTP model.

Towards fine-grained urban traffic knowledge extraction using mobile sensing

We introduce our vision for mining fine-grained urban traffic knowledge from mobile sensing, especially GPS location traces. Beyond characterizing human mobility patterns and measuring traffic congestion, we show how mobile sensing can also reveal details such as intersection performance statistics that are useful for optimizing the timing of a traffic signal. Realizing such applications requires co-designing privacy protection algorithms and novel traffic modeling techniques so that the needs for privacy preserving and traffic modeling can be simultaneously satisfied. We explore privacy algorithms based on the virtual trip lines (VTL) concept to regulate where and when the mobile data should be collected. The traffic modeling techniques feature an integration of traffic principles and learning/optimization techniques. The proposed methods are illustrated using two case studies for extracting traffic knowledge for urban signalized intersection.