Environmental impact of combined ITS traffic management strategies

Transport was responsible for 20% of the total greenhouse gas emissions in Europe during 2011 (European Environmental Agency 2013) with road transport being the key contributor. To tackle this, targets have been established in Europe and worldwide to curb transport emissions. This poses a significant challenge on Local Government and transport operators who need to identify a set of effective measures to reduce the environmental impact of road transport and at the same time keep the traffic smooth. Of the road transport pollutants, this paper considers NOx, CO2 and black carbon (BC). A particular focus is put on black carbon, which is formed through incomplete combustion of carboneous materials, as it has a significant impact on the Earth’s climate system. It absorbs solar radiation, influences cloud processes, and alters the melting of snow and ice cover (Bond et al. 2013). BC also causes serious health concerns: black carbon is associated with asthma and other respiratory problems, heart attacks and lung cancer (Sharma 2010; United States Environmental Protection Agency 2012). Since BC emissions are mainly produced during the decelerating and accelerating phases (Zhang et al. 2009), ITS actions able to reduce stop&go phases have the potential to reduce BC emissions. This paper investigates the effectiveness of combined ITS actions in urban context in reducing CO2 and BC emissions and improving traffic conditions

Impact of traffic management on black carbon emissions: a microsimulation study

This paper investigates the effectiveness of traffic management tools, includ- ing traffic signal control and en-route navigation provided by variable message signs (VMS), in reducing traffic congestion and associated emissions of CO2, NOx, and black carbon. The latter is among the most significant contributors of climate change, and is associated with many serious health problems. This study combines traffic microsimulation (S-Paramics) with emission modeling (AIRE) to simulate and predict the impacts of different traffic management measures on a number traffic and environmental Key Performance Indicators (KPIs) assessed at different spatial levels. Simulation results for a real road network located in West Glasgow suggest that these traffic management tools can bring a reduction in travel delay and BC emission respectively by up to 6 % and 3 % network wide. The improvement at local levels such as junctions or corridors can be more significant. However, our results also show that the potential benefits of such interventions are strongly dependent on a number of factors, including dynamic demand profile, VMS compliance rate, and fleet composition. Extensive discussion based on the simulation results as well as managerial insights are provided to support traffic network operation and control with environmental goals. The study described by this paper was conducted under the support of the FP7-funded CARBOTRAF project

Modeling Decision Making Related to Incident Delays During Hurricane Evacuations

Successful evacuations from metropolitan areas require optimizing the transportation network, monitoring conditions, and adapting to changes. Evacuation plans seek to maximize the city\u27s ability to evacuate traffic to flee the endangered region, but once an evacuation begins, real time events degrade even the best plans. To better understand behavioral responses made during a hurricane evacuation, a survey of potential evacuees obtained data on demographics, driving characteristics, and the traffic information considered prior to and during an evacuation. Analysis showed significant levels of correlation between demographic factors (e.g., gender, age, social class, etc.) and self-assessed driver characteristics, but limited correlation with the decision to take an alternate route. Survey results suggest evacuees\u27 decisions to divert are functions of the length of time a driver has been in congestion, the amount of travel information provided, and its method of delivery. This association differs significantly from those identified by other studies that focused on routine, non-evacuation, conditions. A decision-making model that forecasts decision tendencies using these factors was created. The model was integrated in and tested using a dynamic evacuation simulation. The combined model and simulation allow assessment of the impacts traveler information content, timing, and method of delivery have on traffic flow and evacuation times, imitating the impact of traffic information systems. The effectiveness of alternate route use was assessed by measurements of total vehicle volumes processed and queue persistence. Effectiveness was highly dependent on the road network in the immediate vicinity, especially the number of accesses to the alternate route and vehicle capacity on the alternate route and accesses. Integration of the decision-making model in a dynamic hurricane evacuation simulation is unique to this study. This study yields a greater understanding of evacuee decisions and factors associated with related travel decisions. It provides the novel integration of a behavioral model and a dynamic evacuation simulation, increasing the realism of evacuation planning and providing a valuable tool supporting the decision process. Understanding gained may contribute to reduced evacuation times and enhanced public safety

Participatory Road Design: An Investigation into Improving Roads, Drivers' Attitude and Behaviour Using Partiticipatory Design

Improving road safety is currently based mostly on Education, Enforcement and Engineering or the 3 Es. Despite these measures having saved millions of lives since their inception in around 1915, millions of people are still injured or killed in accidents worldwide annually. One relatively unexplored area is the use of driver's tacit (unspoken) knowledge to help in the reduction of accidents, particularly in the area of speed management. Participatory design may offer a way to help utilise drivers' tacit (hidden) knowledge for the improvement of speed management and road safety techniques in a positive and ethical manner. Involvement in the process may also aid in the improvement of drivers' behaviour and attitudes. Previous research in participatory design indicates that the benefits of participatory design are quick acceptance of new designs and innovative solutions to difficult problems, as well as a sense of ownership of the new artefact. My research has investigated the efficacy of using participatory design in road safety. This was done by having participants take part series of four different types of workshops aimed at improving driver behaviour and attitudes as well as road design using models. The research involved a total of 105 participants with group sizes ranging from 3 to 28 people. It was found that participatory design workshops were capable of: allowing people to redesign a variety of roads and improve them by reducing their estimated speeds, without adversely affecting other ratings such as safety, aesthetics, preference and liveability; improving self reported driver behaviour; and allowing the interaction of people from various backgrounds in a positive and stimulating environment. Workshops were also rated highly as a teaching and design tool by all those involved in the process. Finally, unlike standard participatory design processes, some workshops also included more than just the design team with the inclusion of additional participants as audience members. This was also found to be a practical method of including more people in the participatory design process without reducing the effectiveness of the process

What kind of information do drivers need? An investigation of drivers' information requirements in Kuala Lumpur, Malaysia

Past research indicated that driver information requirements were varied (e. g. Spyridakis et al., 1991) and the motorists population cannot be consider homogeneous in terms of information requirements (e. g. Haselkorn et al., 1991). Some of the previous studies even suggestedth at before the so-called intelligent systemsg o into production, several unresolved issues concerning what kind of information drivers require need to be resolved. Thus, this thesis is interested in exploring several human factors issues concerning drivers; ' information requirements. First, the study is trying to provide at least a general picture of what kind of information is suitable to be presented to drivers in certain types of journey. Secondly, the thesis is interested in exploring the suitable timing and mode to present the required information to the target audiences. Besides the aforementioned human factors issues, this research also investigated how drivers plan their routes and find their way in unfamiliar destinations. The study is also interested in examining criteria used by drivers in choosing a route to their intended destination. Finally, this thesis aims to measure respondents' behavioral responses when they were given several traffic messages on congestion while commuting to and from work. The results also revealed that local drivers used more than one strategy for route planning and wayfinding in unfamiliar locations. Maps were the main strategy used by most of the respondents who participated in this study. Other strategies used by respondents were asking a passer-by, relying on memory and going without preparation. Apart from that, this study also demonstrated the difficulty in arriving at a general conclusion concerning the appropriate criteria that drivers would use in selecting a route for different trips. Local drivers would use a variety types of criteria in order to choose a route to a particular destination. However, the thesis identifies that drivers mainly employed three types of criteria in selecting a route to a particular destination. These criteria were safety, saving mileage and avoiding congested routes The final study (Study 3) was interested in extending the results of both studies I and 2 particularly the presentationo f congestionm essagesto its end users,i . e. motorists. An experiment was conducted to investigate drivers' response towards the presentation of traffic messagesa bout congestion.T he findings clearly supportedp revious work that found different types of information are likely to elicit different kind of responses from the drivers. In addition, local drivers also had ideas about the design of future traffic messages on congestion. For example, the need to have a quick solution when faced with the problem, e. g. offer alternate route; the need to have information on travel time if they decided to use the alternate route recommended by the systems; and some of the messages should be given as early as possible to serve as pre-trip advanced warning to drivers. The findings clearly demonstrated the preference for having more information rather than less

Manipulation of Online Reviews: Analysis of Negative Reviews for Healthcare Providers

There is a growing reliance on online reviews in today’s digital world. As the influence of online reviews amplified in the competitive marketplace, so did the manipulation of reviews and evolution of fake reviews on these platforms. Like other consumer-oriented businesses, the healthcare industry has also succumbed to this phenomenon. However, health issues are much more personal, sensitive, complicated in nature requiring knowledge of medical terminologies and often coupled with myriad of interdependencies. In this study, we collated the literature on manipulation of online reviews, identified the gaps and proposed an approach, including validation of negative reviews of the 500 doctors from three different states: New York and Arizona in USA and New South Wales in Australia from the RateMDs website. The reviews of doctors was collected, which includes both numerical star ratings (1-low to 5-high) and textual feedback/comments. Compared to other existing research, this study will analyse the textual feedback which corresponds to the clinical quality of doctors (helpfulness and knowledge criteria) rather than process quality experiences. Our study will explore pathways to validate the negative reviews for platform provider and rank the doctors accordingly to minimise the risks in healthcare

Improving Traffic Safety And Drivers\u27 Behavior In Reduced Visibility Conditions

This study is concerned with the safety risk of reduced visibility on roadways. Inclement weather events such as fog/smoke (FS), heavy rain (HR), high winds, etc, do affect every road by impacting pavement conditions, vehicle performance, visibility distance, and drivers’ behavior. Moreover, they affect travel demand, traffic safety, and traffic flow characteristics. Visibility in particular is critical to the task of driving and reduction in visibility due FS or other weather events such as HR is a major factor that affects safety and proper traffic operation. A real-time measurement of visibility and understanding drivers’ responses, when the visibility falls below certain acceptable level, may be helpful in reducing the chances of visibility-related crashes. In this regard, one way to improve safety under reduced visibility conditions (i.e., reduce the risk of visibility related crashes) is to improve drivers’ behavior under such adverse weather conditions. Therefore, one of objectives of this research was to investigate the factors affecting drivers’ stated behavior in adverse visibility conditions, and examine whether drivers rely on and follow advisory or warning messages displayed on portable changeable message signs (CMS) and/or variable speed limit (VSL) signs in different visibility, traffic conditions, and on two types of roadways; freeways and two-lane roads. The data used for the analyses were obtained from a self-reported questionnaire survey carried out among 566 drivers in Central Florida, USA. Several categorical data analysis techniques such as conditional distribution, odds’ ratio, and Chi-Square tests were applied. In addition, two modeling approaches; bivariate and multivariate probit models were estimated. The results revealed that gender, age, road type, visibility condition, and familiarity with VSL signs were the significant factors affecting the likelihood of reducing speed following CMS/VSL instructions in reduced visibility conditions. Other objectives of this survey study were to determine the content of messages that iv would achieve the best perceived safety and drivers’ compliance and to examine the best way to improve safety during these adverse visibility conditions. The results indicated that Caution-fog ahead-reduce speed was the best message and using CMS and VSL signs together was the best way to improve safety during such inclement weather situations. In addition, this research aimed to thoroughly examine drivers’ responses under low visibility conditions and quantify the impacts and values of various factors found to be related to drivers’ compliance and drivers’ satisfaction with VSL and CMS instructions in different visibility and traffic conditions. To achieve these goals, Explanatory Factor Analysis (EFA) and Structural Equation Modeling (SEM) approaches were adopted. The results revealed that drivers’ satisfaction with VSL/CMS was the most significant factor that positively affected drivers’ compliance with advice or warning messages displayed on VSL/CMS signs under different fog conditions followed by driver factors. Moreover, it was found that roadway type affected drivers’ compliance to VSL instructions under medium and heavy fog conditions. Furthermore, drivers’ familiarity with VSL signs and driver factors were the significant factors affecting drivers’ satisfaction with VSL/CMS advice under reduced visibility conditions. Based on the findings of the survey-based study, several recommendations are suggested as guidelines to improve drivers’ behavior in such reduced visibility conditions by enhancing drivers’ compliance with VSL/CMS instructions. Underground loop detectors (LDs) are the most common freeway traffic surveillance technologies used for various intelligent transportation system (ITS) applications such as travel time estimation and crash detection. Recently, the emphasis in freeway management has been shifting towards using LDs data to develop real-time crash-risk assessment models. Numerous v studies have established statistical links between freeway crash risk and traffic flow characteristics. However, there is a lack of good understanding of the relationship between traffic flow variables (i.e. speed, volume and occupancy) and crashes that occur under reduced visibility (VR crashes). Thus, another objective of this research was to explore the occurrence of reduced visibility related (VR) crashes on freeways using real-time traffic surveillance data collected from loop detectors (LDs) and radar sensors. In addition, it examines the difference between VR crashes to those occurring at clear visibility conditions (CV crashes). To achieve these objectives, Random Forests (RF) and matched case-control logistic regression model were estimated. The results indicated that traffic flow variables leading to VR crashes are slightly different from those variables leading to CV crashes. It was found that, higher occupancy observed about half a mile between the nearest upstream and downstream stations increases the risk for both VR and CV crashes. Moreover, an increase of the average speed observed on the same half a mile increases the probability of VR crash. On the other hand, high speed variation coupled with lower average speed observed on the same half a mile increase the likelihood of CV crashes. Moreover, two issues that have not explicitly been addressed in prior studies are; (1) the possibility of predicting VR crashes using traffic data collected from the Automatic Vehicle Identification (AVI) sensors installed on Expressways and (2) which traffic data is advantageous for predicting VR crashes; LDs or AVIs. Thus, this research attempts to examine the relationships between VR crash risk and real-time traffic data collected from LDs installed on two Freeways in Central Florida (I-4 and I-95) and from AVI sensors installed on two vi Expressways (SR 408 and SR 417). Also, it investigates which data is better for predicting VR crashes. The approach adopted here involves developing Bayesian matched case-control logistic regression using the historical VR crashes, LDs and AVI data. Regarding models estimated based on LDs data, the average speed observed at the nearest downstream station along with the coefficient of variation in speed observed at the nearest upstream station, all at 5-10 minute prior to the crash time, were found to have significant effect on VR crash risk. However, for the model developed based on AVI data, the coefficient of variation in speed observed at the crash segment, at 5-10 minute prior to the crash time, affected the likelihood of VR crash occurrence. Argument concerning which traffic data (LDs or AVI) is better for predicting VR crashes is also provided and discussed

Business relationships in the automotive and component industries in Portugal

Partnering has been the most commonly used term to describe collaboration between a buyer and its direct supplier. The automotive industry has been the basis for the development of most studies on the subject. Despite the many studies on partnering, some people share the view that largely missing from the literature is a clear definition of this concept and of how it operates within dyadic (i.e. between a buyer and its direct suppliers), network and firm contexts. This is found to be particularly important if automotive companies geographically spread in the globe are to be properly managed. The purpose of the research presented in this thesis was to explore inter-firm collaboration and partnering between a subsidiary of a motor vehicle manufacturer and its direct suppliers, taking into account the ownership ties of firms, such as those of multinational corporations (MNCs). The objective was to generate new knowledge on how inter-firm collaboration and partnering operate and on the factors that influence the business relationships that are established between the referred companies. The researcher followed a single case study research strategy in order to develop a new and empirically grounded understanding, while favouring contextualisation and complexity. The researcher adopted a triangulated research design in which quantitative and qualitative data were gathered in two stages, through a self-administered mailed questionnaire and in-depth interviews, respectively. The findings suggest that: (a) relationships can be characterised by several dimensions, (i.e. commitment, trust, win-win, long-term orientation, co-ordination, joint problem solving, flexibility, mutual dependence) each of which is a mix of collaborative and non-collaborative elements; (b) a diversified scenario of relationships can be explained by the different combinations of several contextual factors (i.e. organisational, relational, spatial and network); the importance of each needs to be weighted and hierarchised; (c) the network affects both to enable and constrain the freedom of action at the level of the customer supplier dyad; and (d) partnering is contingent on the position, role and influence at different points in the network. The research argues that relationship management can be enhanced through the application of analytical tools to the assessment of business relationships. New frameworks for analysis are presented as significant contributions to knowledge, among a series of theoretical, methodological and empirical contributions. The researcher suggests directions for research which will further enhance the understanding of inter-firm collaboration and partnering and business relationships within a multinational network context