Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

A model of mistaken alibis: How innocent alibi providers generate inaccurate alibis

This dissertation comprised two experiments, which addressed three main goals: (a) to test a new paradigm for measuring objectively the accuracy of alibis, (b) to explore the effectiveness of three retrieval cues (time only, location only, and time-and-location) in an alibi context, and (c) to explore the metacognitive strategies of innocent alibi providers who experience different financial incentives as well as different motivations for reporting (be informative vs. be convincing). ^ The novel paradigm appears promising: by surreptitiously controlling the whereabouts of future alibi providers during a critical time, objective accuracy measurements were in fact possible. Such accuracy measurements revealed that time-cued retrieval can be devastating to innocent alibi providers. Participants who attempted to recall their whereabouts via a time cue were significantly less accurate than participants who attempted recall via a location cue (Experiment 1). ^ Innocent alibi providers, when cued effectively, may not, however, report their memories differently from memory reporters in non-alibi contexts. When cued effectively, participants who experienced a goal of being convincing did not differ in accuracy from participants who experienced a goal of merely being informative (Experiment 2). Similarly, participants did not differ from one another in accuracy across different levels of financial incentive (Experiment 2). ^ Despite the indistinguishable accuracy rates of alibi providers and non-alibi memory reporters when retrieval was cued effectively, proffering mistaken alibis presents a real risk for innocent suspects. Future research needs to address methods by which that risk can be reduced. ^

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Enhancement of wireless networks with heterogeneous antennas

In recent years, wireless communication infrastructures have been widely deployed for both personal and business applications. IEEE 802.11 series Wireless Local Area Network (WLAN) standards attract lots of attention due to their low cost and high data rate. Wireless ad hoc networks which use IEEE 802.11 standards are one of hot spots of recent network research. Designing appropriate Media Access Control (MAC) layer protocols is one of the key issues for wireless ad hoc networks. ^ Existing wireless applications typically use omni-directional antennas. When using an omni-directional antenna, the gain of the antenna in all directions is the same. Due to the nature of the Distributed Coordination Function (DCF) mechanism of IEEE 802.11 standards, only one of the one-hop neighbors can send data at one time. Nodes other than the sender and the receiver must be either in idle or listening state, otherwise collisions could occur. The downside of the omni-directionality of antennas is that the spatial reuse ratio is low and the capacity of the network is considerably limited. ^ It is therefore obvious that the directional antenna has been introduced to improve spatial reutilization. As we know, a directional antenna has the following benefits. It can improve transport capacity by decreasing interference of a directional main lobe. It can increase coverage range due to a higher SINR (Signal Interference to Noise Ratio), i.e., with the same power consumption, better connectivity can be achieved. And the usage of power can be reduced, i.e., for the same coverage, a transmitter can reduce its power consumption. ^ To utilizing the advantages of directional antennas, we propose a relay-enabled MAC protocol. Two relay nodes are chosen to forward data when the channel condition of direct link from the sender to the receiver is poor. The two relay nodes can transfer data at the same time and a pipelined data transmission can be achieved by using directional antennas. The throughput can be improved significant when introducing the relay-enabled MAC protocol. ^ Besides the strong points, directional antennas also have some explicit drawbacks, such as the hidden terminal and deafness problems and the requirements of retaining location information for each node. Therefore, an omni-directional antenna should be used in some situations. The combination use of omni-directional and directional antennas leads to the problem of configuring heterogeneous antennas, i e., given a network topology and a traffic pattern, we need to find a tradeoff between using omni-directional and using directional antennas to obtain a better network performance over this configuration. ^ Directly and mathematically establishing the relationship between the network performance and the antenna configurations is extremely difficult, if not intractable. Therefore, in this research, we proposed several clustering-based methods to obtain approximate solutions for heterogeneous antennas configuration problem, which can improve network performance significantly. ^ Our proposed methods consist of two steps. The first step (i.e., clustering links) is to cluster the links into different groups based on the matrix-based system model. After being clustered, the links in the same group have similar neighborhood nodes and will use the same type of antenna. The second step (i.e., labeling links) is to decide the type of antenna for each group. For heterogeneous antennas, some groups of links will use directional antenna and others will adopt omni-directional antenna. Experiments are conducted to compare the proposed methods with existing methods. Experimental results demonstrate that our clustering-based methods can improve the network performance significantly. ^