research report
Project included three major parts: 1) field measurements of particulate matter in five urban areas, 2) laboratory modeling of flow and dispersion within model urban areas, and 3) numerical modeling. Project website and database are located at http://emissions.engr.ucr.edu/.
research report |
Aug 2010
published journal article |
Aug 2011
published journal article |
Jan 2013
conference paper
conference paper
working paper
Proper prediction of traffic flow parameters is an essential component of any proactive traffic control system and one of the pillars of advanced management of dynamic traffic networks. In this paper, we present a new short term traffic flow prediction system based on an advanced Time Delay Neural Network (TDNN) model, the structure of which is optimized using a Genetic Algorithm (GA). After presentation of the model’s development, its performance is validated using both simulated and real traffic flow data obtained from the California Testbed in Orange County, California. The model predicts flow and occupancy values at a given freeway site based on contributions from their recent temporal profile as well the spatial contribution from neighboring sites. Both temporal and spatial effects were found essential for proper prediction. An in-depth investigation of the variables pertinent to traffic flow prediction was conducted examining the extent of the “look-back” interval, the extent of prediction in the future, the extent of spatial contribution, the resolution of the input data, and their effects on prediction accuracy. Results obtained indicate that the prediction errors vary inversely with the extent of the spatial contribution, and that the inclusion of three loop stations in both directions of the subject station is sufficient for practical purposes. Also, the longer the extent of prediction, the more the predicted values tend toward the mean of the actual, for which case the optimal look-back interval also shortens. Interestingly, it was found that coarser data resolution is better for longer extents of prediction. The implication is that the level of data aggregation/resolution should be comparable to the prediction horizon for best accuracy. The model performed acceptably using both simulated and real data. The model also showed potential to be superior to such other well-known neural network models as the Multi layer Feed-forward (MLF) when applied to the same problem. Keywords: Traffic Flow Prediction, Neural Networks, Genetic Algorithms, Traffic Management.
working paper |
Nov 1995
research report |
Sep 2001
research report |
Sep 1993
research report |
Sep 1997
working paper |
Jun 1999
working paper
To better understand how road congestion adversely affects trucking operations, we surveyed approximately 1200 managers of all types of trucking companies operating in California. More than 80% of these managers consider traffic congestion on freeways and surface streets to be either a “somewhat serious” or “critically serious” problem for their business. A structural equations model (SEM) is estimated on these data to determine how five aspects of the congestion problem differ across sectors of the trucking industry. The five aspects were slow average speeds, unreliable travel times, increased driver frustration and morale, higher fuel and maintenance costs, and higher costs of accidents and insurance. The model also simultaneously estimates how these five aspects combine to predict the perceived overall magnitude of the problem. Overall, congestion is perceived to be a more serious problem by managers of trucking companies engaged in intermodal operations, particularly private and for-hire trucking companies serving airports and private companies serving rail terminals. Companies specializing in refrigerated transport also perceive congestion to be a more serious overall problem, as do private companies engaged in LTL operations. The most problematic aspect of congestion is unreliable travel times, followed by driver frustration and morale, then by slow average speeds. Unreliable travel times are a significantly more serious problem for intermodal air operations. Driver frustration and morale attributable to congestion is perceived to be more of a problem by managers of long-haul carriers and tanker operations. Slow average speeds are also more of a concern for airport and refrigerated operations.
working paper |
Apr 1999
working paper |
Jan 2000
published journal article
published journal article
published journal article
Wireless communication is being used as an enabling technology with traditional fixed traffic control systems in this transitional era toward Intelligent Transportation Systems (ITS). Unfortunately, major security concerns have arisen with respect to ever-increasing complexity and interconnectivity, and a noticeable lack of attention for security in these systems. Addressing concerns is a colossal challenge as it requires thorough development and formal analysis of a system model with respect to security. To tackle this challenge, we present a novel formal attack modeling and impact analysis methodology based on the Link Queue Model (LQM) of traffic flow inside a double ring road network, which is equivalent to a grid network with homogeneous links. We develop attack models as functions of tampered traffic control settings (e.g., green time ratios, cycle length, retaining ratios) with outputs equivalent to mobility impacts on the traffic network (e.g., time until system reaches state convergence, asymptotic average network flow). Further, for a given attack model, we define and identify vulnerable states: states that are critical to protect because they lead to negative impacts under the given attack model. Using our methodology we found that for certain vulnerable states, after only a few cycles of tampered control settings an attacker could cause a real impact of 1.5x speed-up in gridlock state convergence or 37%-99% drop in the asymptotic average flow rate. These results imply potentially drastic financial costs for cities and all involved drivers if similar attacks were performed on a real traffic control system. (C) 2020 Elsevier Ltd. All rights reserved.
published journal article
Performance indcators are quantitative measures that enable managers and policymakers to monitor the current position of an agency and outline strategies to improve performance. Because public services have many different dimensions of performance, a large number of performance indicators are normally used. In this paper a conceptual model is used to help select a few performance indicators that represent all the important performance concepts. Data were obtained from a national sample of 311 urban bus transit systems in the first year that data were reported under Section 15 of the Urban Mass Transportation Act of 1965, as amended. The steps in the performarice-evaluatlon procedure Involve defining a conceptual model of performance and designing a balancod set of performance indicators that represent all performance concepts. Factor analysis is then used to select the indicators that best represent all dimensions of performance. This smell, representative set of performance Indicators is used to analyze performance and to establish peer-group rankings.
published journal article