Building a bicycle suitability map for Coimbra

The adoption of faster modes of transportation (mainly the private car) has changed profoundly the spatial organisation of cities. The increase in distance covered due to increased speed of travel and to urban sprawl leads to an increase in energy consumption, being the transportation sector a huge consumer responsible for 61.5% of total world oil consumption and a global final energy consumption of 31.6% in EU-27 (2007). Due to unsustainable transportation conditions, many cities suffer from congestion and various other traffic problems. Such situations get worse with solutions mostly seen in the development of new infrastructure for motorized modes of transportation, and construction of car parking structures. The bicycle, considered the most efficient among all modes of transportation including walking, is a travel mode that can be adopted in most cities contributing for urban sustainability given the associated environmental, economic and social advantages. In many nations a large number of policy initiatives have focused on discouraging the use of private cars, encouraging the use of sustainable modes of transportation, like public transportation and other forms such as bicycling. Given the importance of developing initiatives that favour the use of bicycle as an urban transportation mode, an analysis of city suitability, including distances and slopes of street network, is crucial in order to help decision-makers to plan the city for bicycle. In this research Geographical Information Systems (GIS) technology was used for this purpose and some results are presented concerning the city of Coimbra.

Mobile cockpit system for enhanced electric bicycle use

This paper presents the project of a mobile cockpit system (MCS) for smartphones, which provides assistance to electric bicycle (EB) cyclists in smart cities' environment. The presented system introduces a mobile application (MCS App) with the goal to provide useful personalized information to the cyclist related to the EB's use, including EB range prediction considering the intended path, management of the cycling effort performed by the cyclist, handling of the battery charging process, and the provisioning of information regarding available public transport. This work also introduces the EB cyclist profile concept, which is based on historical data analysis previously stored in a database and collected from mobile devices' sensors. From the tests performed, the results show the importance of route guidance, taking into account the energy savings. The results also show significant changes on range prediction based on user and route taken. It is important to say that the proposed system can be used for all bicycles in general.

Crashes, volatility, and the equity premium: Lessons from S&P 500 options

We use a novel pricing model to imply time series of diffusive volatility and jump intensity from S&P 500 index options. These two measures capture the ex ante risk assessed by investors. Using a simple general equilibrium model, we translate the implied measures of ex ante risk into an ex ante risk premium. The average premium that compensates the investor for the ex ante risks is 70% higher than the premium for realized volatility. The equity premium implied from option prices is shown to significantly predict subsequent stock market returns.

Earnings management and stock price crashes post-crossdelisting

We test whether cross-delisted firms from the major U.S. stock exchanges experience an increase in crash risk associated with earnings management. Consistent with our prediction, we find that earnings management have a greater positive impact on stock price crash risk post-cross-delisting when compared to a sample of still cross-listed firms. Moreover, our results suggest that this effect is more pronounced for crossdelisted firms from countries with weaker investor protection and poorer quality of their information environment. We further examine whether managers’ ability to manipulate earnings increases post-cross-delisting around seasoned equity offerings. Our evidence shows that cross-delisted firms that engage in earnings management to inflate reported earnings prior to a seasoned equity offering are more likely to observe a subsequent stock price crash.

Learning about Risk and Return: A Simple Model of Bubbles and Crashes

This paper demonstrates that an asset pricing model with least-squares learning can lead to bubbles and crashes as endogenous responses to the fundamentals driving asset prices. When agents are risk-averse they need to make forecasts of the conditional variance of a stock’s return. Recursive updating of both the conditional variance and the expected return implies several mechanisms through which learning impacts stock prices. Extended periods of excess volatility, bubbles and crashes arise with a frequency that depends on the extent to which past data is discounted. A central role is played by changes over time in agents’ estimates of risk.

The Effect of Four-Lane to Three-Lane Conversion on the Number of Crashes and Crash Rates in Iowa Roads, June 2005

We analyze crash data collected by the Iowa Department of Transportation using Bayesian methods. The data set includes monthly crash numbers, estimated monthly traffic volumes, site length and other information collected at 30 paired sites in Iowa over more than 20 years during which an intervention experiment was set up. The intervention consisted in transforming 15 undivided road segments from four-lane to three lanes, while an additional 15 segments, thought to be comparable in terms of traffic safety-related characteristics were not converted. The main objective of this work is to find out whether the intervention reduces the number of crashes and the crash rates at the treated sites. We fitted a hierarchical Poisson regression model with a change-point to the number of monthly crashes per mile at each of the sites. Explanatory variables in the model included estimated monthly traffic volume, time, an indicator for intervention reflecting whether the site was a “treatment” or a “control” site, and various interactions. We accounted for seasonal effects in the number of crashes at a site by including smooth trigonometric functions with three different periods to reflect the four seasons of the year. A change-point at the month and year in which the intervention was completed for treated sites was also included. The number of crashes at a site can be thought to follow a Poisson distribution. To estimate the association between crashes and the explanatory variables, we used a log link function and added a random effect to account for overdispersion and for autocorrelation among observations obtained at the same site. We used proper but non-informative priors for all parameters in the model, and carried out all calculations using Markov chain Monte Carlo methods implemented in WinBUGS. We evaluated the effect of the four to three-lane conversion by comparing the expected number of crashes per year per mile during the years preceding the conversion and following the conversion for treatment and control sites. We estimated this difference using the observed traffic volumes at each site and also on a per 100,000,000 vehicles. We also conducted a prospective analysis to forecast the expected number of crashes per mile at each site in the study one year, three years and five years following the four to three-lane conversion. Posterior predictive distributions of the number of crashes, the crash rate and the percent reduction in crashes per mile were obtained for each site for the months of January and June one, three and five years after completion of the intervention. The model appears to fit the data well. We found that in most sites, the intervention was effective and reduced the number of crashes. Overall, and for the observed traffic volumes, the reduction in the expected number of crashes per year and mile at converted sites was 32.3% (31.4% to 33.5% with 95% probability) while at the control sites, the reduction was estimated to be 7.1% (5.7% to 8.2% with 95% probability). When the reduction in the expected number of crashes per year, mile and 100,000,000 AADT was computed, the estimates were 44.3% (43.9% to 44.6%) and 25.5% (24.6% to 26.0%) for converted and control sites, respectively. In both cases, the difference in the percent reduction in the expected number of crashes during the years following the conversion was significantly larger at converted sites than at control sites, even though the number of crashes appears to decline over time at all sites. Results indicate that the reduction in the expected number of sites per mile has a steeper negative slope at converted than at control sites. Consistent with this, the forecasted reduction in the number of crashes per year and mile during the years after completion of the conversion at converted sites is more pronounced than at control sites. Seasonal effects on the number of crashes have been well-documented. In this dataset, we found that, as expected, the expected number of monthly crashes per mile tends to be higher during winter months than during the rest of the year. Perhaps more interestingly, we found that there is an interaction between the four to three-lane conversion and season; the reduction in the number of crashes appears to be more pronounced during months, when the weather is nice than during other times of the year, even though a reduction was estimated for the entire year. Thus, it appears that the four to three-lane conversion, while effective year-round, is particularly effective in reducing the expected number of crashes in nice weather.

The dog that did not bark: Insider trading and crashes

This paper documents that at the individual stock level insiders sales peak many months before a large drop in the stock price, while insiders purchases peak only the month before a large jump. We provide a theoretical explanation for this phenomenon based on trading constraints and asymmetric information. We test our hypothesis against competing stories such as patterns of insider trading driven by earnings announcement dates, or insiders timing their trades to evade prosecution. Finally we provide new evidence regarding crashes and the degree of information asymmetry.

Historical Summary of Travel, Crashes, Fatalities, and Rates 1970 – 2009, July 6, 2010

Historical Summary of Travel, Crashes, Fatalities, and Rates 1970 – 2009, produced by Iowa Department of Transportation.

Historical Summary of Travel, Crashes, Fatalities, and Rates 2001 – 2009, July 6, 2010

Historical Summary of Travel, Crashes, Fatalities, and Rates 2001 – 2009, produced by Iowa Department of Transportation.

Strategies to Address Nighttime Crashes at Rural, Unsignalized Intersections, TR-540, 2008

Citizens request the installation of roadway lighting in their communities based on several motivations, including the experience or perception that lighting improves traffic safety and reduces crime, while also providing a tangible benefit of taxpayer dollars at work. Roadway authority staff fully appreciate these citizen concerns; however, roadway lighting is expensive to install, supply energy to, and maintain in perpetuity. The installation of roadway lighting is only one of a number of strategies agencies have to address nighttime crash concerns. This research assists local agencies in deciding when, where, and how much rural intersection lighting to provide.

Iowa 2015-2016 Bicycle Map, 2015

Iowa has more than 1,800 miles of beautiful trails available for a variety of uses, including bicycling, hiking/running, skating, equestrian use, cross-country skiing, snowmobiling, and photography/nature study. This map lists and highlights 60 trails of 5 miles length or great. It also indicates where shorter trails exist. This record contains PDFs of the full front and back of the map. Inset maps of 16 major cities and their trail systems are included in this record.

Predicting Market Crashes: An Examination of the Performance of Macroeconomic Indicators and Technical Analysis

The purpose of this study is to examine macroeconomic indicators‟ and technical analysis‟ ability to signal market crashes. Indicators examined were Yield Spread, The Purchasing Managers Index and the Consumer Confidence Index. Technical Analysis indicators were moving average, Moving Average Convergence-Divergence and Relative Strength Index. We studied if commonly used macroeconomic indicators can be used as a warning system for a stock market crashes as well. The hypothesis is that the signals of recession can be used as signals of stock market crash and that way a basis for a hedging strategy. The data is collected from the U.S. markets from the years 1983-2010. Empirical studies show that macroeconomic indicators have been able to explain the future GDP development in the U.S. in research period and they were statistically significant. A hedging strategy that combined the signals of yield spread and Consumer Confidence Index gave most useful results as a basis of a hedging strategy in selected time period. It was able to outperform buy-and-hold strategy as well as all of the technical indicator based hedging strategies.