Arrested Drunk Drivers : Trends, social background, recidivism and mortality

The aim of this study was to examine the trends, incidence and recidivism of drunken driving during a 20-year period (1988 - 2007) using the data on all suspected drunken driving in this period. Furthermore, the association between social background and drunken driving, and the mortality of drunk drivers were studied by using administrative register data provided by Statistics Finland. The study was completely register-based. In 1989 - 1991, every year 30,000 drivers were suspected of drunken driving, but the number fell to less than 20,000 by 1994, during the economic recession. The changes in the arrest incidence of the youngest age groups were especially pronounced, most of all in the age group of 18 - 19-year olds. Even though the incidence among youth decreased dramatically, their incidence rate was still twice that of the general population aged 15 - 84 years. Drunken driving was associated with a poor social background among youth and working-aged men and women. For example, a low level of education, unemployment, divorce, and parental factors in youth were associated with a higher risk of being arrested for drunken driving. While a low income was related to more drunken driving among working-aged people, the effect among young persons was the opposite. Every third drunk driver got rearrested during a 15-year period, whereas the estimated rearrest rate was 44%. Findings of drugs only or in combination with alcohol increased the risk of rearrest. The highest rearrest rates were seen among drivers who were under the influence of amphetamines or cannabis. Also male gender, young age, high blood alcohol concentration, and arrest during weekdays and in the daytime predicted rearrest. When compared to the general population, arrested drunk drivers had significant excess mortality. The greatest relative differences were seen in alcohol-related causes of death (including alcohol diseases and alcohol poisoning), accidents, suicides and violence. Also mortality due to other than alcohol-related diseases was elevated among drunk drivers. Drunken driving was associated with multiple factors linked to traffic safety, health and social problems. Social marginalization may expose a person to harmful use of alcohol and drunken driving, and the associations are seen already among the youth. Recidivism is common among drunk drivers, and driving under the influence of illicit and/or medicinal drugs is likely to indicate worse substance abuse problems, judging from the high rearrest rates. High alcohol-related mortality in this population shows that drunken driving is clearly an indicator of alcohol abuse. More effective measures of preventing alcohol-related harms are needed, than merely preventing convicted drunk drivers from driving again.

Macroscopic model of city traffic using bond graph modelling

Modelling of city traffic involves capturing of all the dynamics that exist in real-time traffic. Probabilistic models and queuing theory have been used for mathematical representation of the traffic system. This paper proposes the concept of modelling the traffic system using bond graphs wherein traffic flow is based on energy conservation. The proposed modelling approach uses switched junctions to model complex traffic networks. This paper presents the modelling, simulation and experimental validation aspects.

Professional Driving : An Emphasis on Organizational Safety Climate

According to the literature and statistical figures, professional drivers constitute a high-risk group in traffic and should be investigated in connection with the factors related to safe driving. However, safety-related behaviours and outcomes among professional drivers have attracted very little attention from safety researchers. In addition, comparing different professional and non-professional driver groups in terms of critical on-the-road characteristics and outcomes has been indicated in the literature as being necessary for a more comprehensive understanding of driver groups and the nature of driving itself. The aim of the present study was to investigate professional driving from a safety climate stand point in relation to predominant driving-related factors and by considering the differences between driver groups. Hence, four Sub-studies were conducted according to a framework emphasizing the relationships between safety climate, driver groups, driver stress, human factors (i.e., driver behaviour and performance) and accidents. Demographic information, as well as data for driver behaviour, performance, and driver stress was collected by questionnaire. The data was analysed using factor analysis, analysis of covariance as well as hierarchical and logistic regression analysis. The results revealed multi-dimensional factor structures for the safety climate measures. Considering the relationships between variables, differences were evidenced regarding on-the-road stress reactions, risky driver behaviours and penalties, between the various professional and non-professional driver groups. Driver stress was found to be related to accidents. The results also indicated that the safety climate has positive relationships with both driver behaviour and performance, and as well as involvement in accidents. The present study has a number of critical implications resulting from the fact that the way in which the effects of safety climate on professional driving were investigated, as well as the differences between professional and non-professional driver groups, was unique. Additionally, for the first time, a safety climate scale was developed specifically for professional drivers. According to the results of the study and to previous literature, a tentative model was proposed representing a possible route for the relationships between safety climate, human factors, driver stress, driver groups and accidents, by emphasizing the effects of safety climate.

The effect of traffic shaping in efficiently providing end-to-end performance guarantees

This paper reports new results concerning the capabilities of a family of service disciplines aimed at providing per-connection end-to-end delay (and throughput) guarantees in high-speed networks. This family consists of the class of rate-controlled service disciplines, in which traffic from a connection is reshaped to conform to specific traffic characteristics, at every hop on its path. When used together with a scheduling policy at each node, this reshaping enables the network to provide end-to-end delay guarantees to individual connections. The main advantages of this family of service disciplines are their implementation simplicity and flexibility. On the other hand, because the delay guarantees provided are based on summing worst case delays at each node, it has also been argued that the resulting bounds are very conservative which may more than offset the benefits. In particular, other service disciplines such as those based on Fair Queueing or Generalized Processor Sharing (GPS), have been shown to provide much tighter delay bounds. As a result, these disciplines, although more complex from an implementation point-of-view, have been considered for the purpose of providing end-to-end guarantees in high-speed networks. In this paper, we show that through ''proper'' selection of the reshaping to which we subject the traffic of a connection, the penalty incurred by computing end-to-end delay bounds based on worst cases at each node can be alleviated. Specifically, we show how rate-controlled service disciplines can be designed to outperform the Rate Proportional Processor Sharing (RPPS) service discipline. Based on these findings, we believe that rate-controlled service disciplines provide a very powerful and practical solution to the problem of providing end-to-end guarantees in high-speed networks.

Queuing strategies for qos management of traffic between lans interconnected by low-speed wan links

We provide a comparative performance evaluation of packet queuing and link admission strategies for low-speed wide area network Links (e.g. 9600 bps, 64 kbps) that interconnect relatively highspeed, connectionless local area networks (e.g. 10 Mbps). In particular, we are concerned with the problem of providing differential quality of service to interLAN remote terminal and file transfer sessions, and throughput fairness between interLAN file transfer sessions. We use analytical and simulation models to study a variety of strategies. Our work also serves to address the performance comparison of connectionless vs. connection-oriented interconnection of CLNS LANS. When provision of priority at the physical transmission level is not feasible, we show, for low-speed WAN links (e.g. 9600 bps), the superiority of connection-oriented interconnection of connectionless LANs, with segregation of traffic streams with different QoS requirements into different window flow controlled connections. Such an implementation can easily be obtained by transporting IP packets over an X.25 WAN. For 64 kbps WAN links, there is a drop in file transfer throughputs, owing to connection overheads, but the other advantages are retained, The same solution also helps to provide throughput fairness between interLAN file transfer sessions. We also provide a corroboration of some of our modelling results with results from an experimental test-bed.

Saturation throughput analysis of an input queueing ATM switch with multiclass bursty traffic

In this paper we consider an N x N non-blocking, space division ATM switch with input cell queueing. At each input, the cell arrival process comprises geometrically distributed bursts of consecutive cells for the various outputs. Motivated by the fact that some input links may be connected to metropolitan area networks, and others directly to B-ISDN terminals, we study the situation where there are two classes of inputs with different values of mean burst length. We show that when inputs contend for an output, giving priority to an input with smaller expected burst length yields a saturation throughput larger than if the reverse priority is given. Further, giving priority to less bursty traffic can give better throughput than if all the inputs were occupied by this less bursty traffic. We derive the asymptotic (as N --> infinity) saturation throughputs for each priority class.

Reinforcement Learning With Function Approximation for Traffic Signal Control

We propose, for the first time, a reinforcement learning (RL) algorithm with function approximation for traffic signal control. Our algorithm incorporates state-action features and is easily implementable in high-dimensional settings. Prior work, e. g., the work of Abdulhai et al., on the application of RL to traffic signal control requires full-state representations and cannot be implemented, even in moderate-sized road networks, because the computational complexity exponentially grows in the numbers of lanes and junctions. We tackle this problem of the curse of dimensionality by effectively using feature-based state representations that use a broad characterization of the level of congestion as low, medium, or high. One advantage of our algorithm is that, unlike prior work based on RL, it does not require precise information on queue lengths and elapsed times at each lane but instead works with the aforementioned described features. The number of features that our algorithm requires is linear to the number of signaled lanes, thereby leading to several orders of magnitude reduction in the computational complexity. We perform implementations of our algorithm on various settings and show performance comparisons with other algorithms in the literature, including the works of Abdulhai et al. and Cools et al., as well as the fixed-timing and the longest queue algorithms. For comparison, we also develop an RL algorithm that uses full-state representation and incorporates prioritization of traffic, unlike the work of Abdulhai et al. We observe that our algorithm outperforms all the other algorithms on all the road network settings that we consider.

Throughput Maximization of Delay-Constrained Traffic in Wireless Energy Harvesting Sensors

A wireless Energy Harvesting Sensor (EHS) needs to send data packets arriving in its queue over a fading channel at maximum possible throughput while ensuring acceptable packet delays. At the same time, it needs to ensure that energy neutrality is satisfied, i.e., the average energy drawn from a battery should equal the amount of energy deposited in it minus the energy lost due to the inefficiency of the battery. In this work, a framework is developed under which a system designer can optimize the performance of the EHS node using power control based on the current channel state information, when the EHS node employs a single modulation and coding scheme and the channel is Rayleigh fading. Optimal system parameters for throughput optimal, delay optimal and delay-constrained throughput optimal policies that ensure energy neutrality are derived. It is seen that a throughput optimal (maximal) policy is packet delay-unbounded and an average delay optimal (minimal) policy achieves negligibly small throughput. Finally, the influence of the harvested energy profile on the performance of the EHS is illustrated through the example of solar energy harvesting.

Analytical models for energy consumption in infrastructure WLAN STAs carrying TCP traffic

We develop analytical models for estimating the energy spent by stations (STAs) in infrastructure WLANs when performing TCP controlled file downloads. We focus on the energy spent in radio communication when the STAs are in the Continuously Active Mode (CAM), or in the static Power Save Mode (PSM). Our approach is to develop accurate models for obtaining the fraction of times the STA radios spend in idling, receiving and transmitting. We discuss two traffic models for each mode of operation: (i) each STA performs one large file download, and (ii) the STAs perform short file transfers. We evaluate the rate of STA energy expenditure with long file downloads, and show that static PSM is worse than just using CAM. For short file downloads we compute the number of file downloads that can be completed with given battery capacity, and show that PSM performs better than CAM for this case. We provide a validation of our analytical models using the NS-2 simulator.

Modeling urban traffic planning thro' strategic decisions- A case from Bangalore, India

This paper deals with reducing the waiting times of vehicles at the traffic junctions by synchronizing the traffic signals. Strategies are suggested for betterment of the situation at different time intervals of the day, thus ensuring smooth flow of traffic. The concept of single way systems are also analyzed. The situation is simulated in Witness 2003 Simulation package using various conventions. The average waiting times are reduced by providing an optimal combination for the traffic signal timer. Different signal times are provided for different times of the day, thereby further reducing the average waiting times at specific junctions/roads according to the experienced demands.

Reinforcement learning with average cost for adaptive control of traffic lights at intersections

We propose for the first time two reinforcement learning algorithms with function approximation for average cost adaptive control of traffic lights. One of these algorithms is a version of Q-learning with function approximation while the other is a policy gradient actor-critic algorithm that incorporates multi-timescale stochastic approximation. We show performance comparisons on various network settings of these algorithms with a range of fixed timing algorithms, as well as a Q-learning algorithm with full state representation that we also implement. We observe that whereas (as expected) on a two-junction corridor, the full state representation algorithm shows the best results, this algorithm is not implementable on larger road networks. The algorithm PG-AC-TLC that we propose is seen to show the best overall performance.

Fuzzy inference based data preprocessing for VBR video traffic prediction

Prediction of variable bit rate compressed video traffic is critical to dynamic allocation of resources in a network. In this paper, we propose a technique for preprocessing the dataset used for training a video traffic predictor. The technique involves identifying the noisy instances in the data using a fuzzy inference system. We focus on three prediction techniques, namely, linear regression, neural network and support vector regression and analyze their performance on H.264 video traces. Our experimental results reveal that data preprocessing greatly improves the performance of linear regression and neural network, but is not effective on support vector regression.

Traffic Profiling for Efficient Network Resource Utilization.

Traffic Engineering has been the prime concern for Internet Service Providers (ISPs), with the main focus being minimization of over-utilization of network capacity even though additional capacity is available which is under-utilized, Furthermore, requirements of timely delivery of digitized audiovisual information raises a new challenge of finding a path meeting these requirements. This paper addresses the issue of (a) distributing load to achieve global efficiency in resource utilization. (b) Finding a path satisfying the real time requirements of, delay and bandwidth requested by the applications. In this paper we do a critical study of the link utilization that varies over time and determine the time interval during which the link occupancy remains constant across days. This information helps in pre-determining link utilization that is useful in balancing load in the network Finally, we run simulations that use a dynamic time interval for profiling traffic and show improvement in terms number of calls admitted/blocked.

Performance of cellular CDMA with voice/data traffic with an SIR based admission control

We analyze the performance of an SIR based admission control strategy in cellular CDMA systems with both voice and data traffic. Most studies In the current literature to estimate CDMA system capacity with both voice and data traf-Bc do not take signal-tlFlnterference ratio (SIR) based admission control into account In this paper, we present an analytical approach to evaluate the outage probability for voice trafllc, the average system throughput and the mean delay for data traffic for a volce/data CDMA system which employs an SIR based admission controL We show that for a dataaniy system, an improvement of about 25% In both the Erlang capacity as well as the mean delay performance is achieved with an SIR based admission control as compared to code availability based admission control. For a mixed voice/data srtem with 10 Erlangs of voice traffic, the Lmprovement in the mean delay performance for data Is about 40%.Ah, for a mean delay of 50 ms with 10 Erlangs voice traffic, the data Erlang capacity improves by about 9%.

Mobile agent based TCP attacker identification in MANET using the traffic history (MAITH)

TCP attacks are the major problem faced by Mobile Ad hoc Networks (MANETs) due to its limited network and host resources. Attacker traceback is a promising solution which allows a victim to identify the exact location of the attacker and hence enables the victim to take proper countermeasure near attack origins, for forensics and to discourage attackers from launching the attacks. However, attacker traceback in MANET is a challenging problem due to dynamic network topology, limited network and host resources such as memory, bandwidth and battery life. We introduce a novel method of TCP attacker Identification in MANET using the Traffic History - MAITH. Based on the comprehensive evaluation based on simulations, we showed that MAITH can successfully track down the attacker under diverse mobile multi-hop network environment with low communication, computation, and memory overhead.