Uncertainty Drivers Research Articles

Lane-Changing Risk Analysis in Undersea Tunnels Based on Fuzzy Inference

Lane-changing in undersea tunnels has a negative impact on the normal traffic flow, and even lays hidden dangers for the occurrence of traffic accidents. Lane-changing behavior in undersea tunnels was divided into free, compulsory, and collaborative lane-changing types according to the characteristics of traffic flow to explore lane-changing risk in undersea tunnels. A fuzzy inference analysis on the three lane-changing behaviors was conducted on the basis of the behavior characteristics of fuzzy uncertainty of drivers. The most representative influencing variables, including speed difference, initial space of vehicles, traffic density, and distance for minimum lane-changing, were selected as fuzzy input variables, and lane-changing risk was used as an output variable to construct fuzzy rules for different lane-changing behavior. Risks of the three lane-changing behaviors were simulated by MATLAB/Simulink. Results demonstrated that the compulsory lane-changing in undersea tunnel was the riskiest, followed by collaborative and free lane-changing. Slope considerably influenced lane-changing risk. Specifically, the lane-changing risk at the downhill section was the highest, and the lane-changing risk at the uphill section was the lowest. The lane-changing risk at the flat section was between them.

Design of a data-driven communication framework as personalized support for users of ADAS.

Recently the automotive industry has made a huge leap forward in Automated Driver Assistance Systems (ADAS) development, increasing the level of driving processes automation. However, ADAS design does not imply any individual support to the driver; this results in a poor understanding of how the ADAS works and its limitations. This type of driver uncertainty regarding ADAS performance can erode the user's trust in the system and result in decreasing situations when the system is in use. This paper presents the design of a data-driven communication framework that can utilize historical and real-time vehicle data to support ADAS users. The data-driven communication framework aims to illustrate the ADAS capabilities and limitations and suggests effective use of the system in real-time driving situations. This type of assistance can improve a driver's understanding of ADAS functionality and encourage its usage.

Discovering Dependencies, Trade‐Offs, and Robustness in Joint Dam Design and Operation: An Ex‐Post Assessment of the Kariba Dam

AbstractGlobally, many countries are actively seeking to maximize the hydropower potential of major river basins, yielding proposals for constructing approximately 3,700 major dams in the near future. At present, economic cost‐benefit analyses are the dominant approach for evaluating candidate dam designs. Yet they typically fail to explore the interdependence of dam design and operation by (i) monetizing and aggregating potentially conflicting and heterogeneous objectives into a single metric and (ii) using predefined operating rules aimed at maximizing specific performance objectives for the dams to be designed. Careful consideration of alternative reservoir operating schemes can help to avoid biases in how optimal dam sizes are identified while also clarifying their capacity to adapt to changing hydroclimatic conditions and human demands. This paper presents an integrated framework to solve coupled dam sizing and operation design problems. The framework combines Multiobjective Robust Decision Making and Evolutionary Multiobjective Direct Policy Search into a novel approach to dam sizing, which internalizes the operation design problem and explicitly considers uncertainty in external drivers. We demonstrate the potential of this integrated dam design framework through an ex‐post design analysis of the Kariba dam in the Zambezi river basin. Our results show that careful exploration of the coupled planning/operation search space yields designs that significantly outperform the existing Kariba system. Moreover, we demonstrate that our framework leads to a significant reduction in capital costs (i.e., smaller reservoir sizes) while simultaneously improving system robustness with respect to changing hydroclimatology and human irrigation demands.

A Method to Project Future Impacts From Threats and Conservation on the Probability of Extinction for North American Migratory Monarch (Danaus plexippus) Populations

The United States Fish and Wildlife Service is currently evaluating the monarch butterfly (Danaus plexippus) for listing under the Endangered Species Act and using the Species Status Assessment (SSA) framework to estimate and forecast drivers that impact the species’ risk of extinction. To evaluate eastern and western monarch populations the monarch SSA built on a foundation of published population models and other literature to identify current growth rates and information on threats and conservation efforts. Here we present the resulting methodology, which aimed to explore the magnitude of monarch population responses to the aggregation of multiple drivers under various scenarios. Our methodology differs from previous research by developing a series of functional cause and effect relationships that link monarch population-specific responses to threats or conservation actions. We incorporated these population-specific responses into stochastic geometric growth models for both eastern and western populations to estimate the probability of quasi-extinction in 50 years. Our models were parameterized using previously estimated population-specific trend data (growth rates and environmental variability) and expert elicited estimates of population responses to multiple drivers (i.e., amount of available breeding and overwintering habitat, insecticide use, migration resource availability, and climate change). We explored plausible future scenarios with realistic place-holder data to evaluate how changes in these drivers influenced monarch quasi-extinction risk for each population. In addition, we captured uncertainty in quasi-extinction risk by calculating cumulative quasi-extinction risk over a full range of quasi-extinction threshold values which were sampled from a uniform distribution bounded by expert-elicited estimates. In both populations, our baseline for comparison was the “current” condition defined by population-specific growth rate and environmental stochasticity from previous research. The result of the methodology presented here is a novel and comprehensive tool that incorporates the impact of future stressors into projections of population numbers over time. The approach provides a tractable and updatable tool that includes multiple types of information and the associated uncertainty of drivers, population impacts, and risk of extinction. For monarchs, this tool will be critical for incorporating the best scientific and commercial information available in the upcoming listing decision.

Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach

The market penetration of Plug-in Electric Vehicles (PEVs) is escalating due to their energy saving and environmental benefits. In order to address PEVs impact on the electric networks, the aggregators need to accurately predict the PEV Travel Behavior (PEV-TB) since the addition of a great number of PEVs to the current distribution network poses serious challenges to the power system. Forecasting PEV-TB is critical because of the high degree of uncertainties in drivers’ behavior. Existing studies mostly simplified the PEV-TB by mapping travel behavior from conventional vehicles. This could cause bias in power estimation considering the differences in PEV-TB because of charging pattern which consequently could bungle economic analysis of aggregators. In this study, to forecast PEV-TB an artificial intelligence-based method -feedforward and recurrent Artificial Neural Networks (ANN) with Levenberg Marquardt (LM) training method based on Rough structure - is developed. The method is based on historical data including arrival time, departure time and trip length. In this study, the correlation among arrival time, departure time and trip length is also considered. The forecasted PEV-TB is then compared with Monte Carlo Simulation (MCS) which is the main benchmarking method in this field. The results comparison depicted the robustness of the proposed methodology. The proposed method reduces the aggregators’ financial loss approximately by 16 $/PEV per year compared to the conventional methods. The findings underline the importance of applying more accurate methods to forecast PEV-TB to gain the most benefit of vehicle electrification in the years to come. • A novel approach is proposed to forecast travel behavior in Plug-in Electric Vehicle. • The method improves the accuracy of travel behavior forecasts by about 10%. • The aggregators’ financial loss and load profile spikes reduced by 23% and 20%. • The findings help to increase market penetration of EVs and lower emissions.

Probabilistic green infrastructure cost calculations using a phased life cycle algorithm integrated with uncertainties

Abstract Green infrastructure (GI) is often considered a cost-effective approach to urban stormwater management. Though various models have been created to simulate the life cycle cost (LCC) and present value (PV) of GI investments, decision-support tools are still few. This paper introduces a probabilistic GI cost estimation algorithm built into the Low Impact Development Rapid Assessment (LIDRA) model. This algorithm tracks annual and cumulative costs associated with the construction, operation and maintenance (O&M), and ultimate replacement of GI systems. In addition, the algorithm accounts for uncertainties in cost drivers, such as a GI's useful life (until replacement), capital and annual O&M costs, inflation, and interest rates. Net present value (NPV) is used to normalize future money flows and cumulative costs of different GI investment scenarios into a comparable current year cost equivalent. Demonstrated at the block scale, the results of the LIDRA algorithm are compared to an MS Excel-based computation of average costs. Variations of uncertainties are then integrated and further explored using an alternative implementation rate. This algorithm is a way to evaluate GI costs considering physical, socioeconomic and life cycle dimensions.

Exploring the impact of signal types and adjacent vehicles on drivers’ choices after the onset of yellow

Drivers’ choices at signalized intersections may be made in great uncertainty after the onset of yellow, which creates potential hazards for road safety. These choices are analyzed and modeled based on field observations at three comparable signalized intersections in Changsha, China. The results show that intersections without monitoring devices widen the indecision zone, which can increase the risk of rear-end collisions and the uncertainty of drivers’ decision-making. In addition, drivers are more likely to stop during the yellow interval at intersections equipped with a green signal countdown device (GSCD) than at those with a green signal flashing device (GSFD). Subsequently, according to the results of a binary logistic regression model (BLRM), drivers’ decision making at the onset of the yellow indication is greatly influenced by the vehicle’s spot speed, the distance to the stop line, and signal and monitoring devices. The presence of an adjacent vehicle with a short space headway can particularly motivate the following driver to make a go-decision after the first driver chooses to pass the intersection. However, a stop-decision by a driver in an adjacent lane can also prompt the following driver to stop.

Отношение к неопределенности и мотивация достижения у водителей разного пола

The article presents a theoretical analysis of cognitive, motivational and emotional components of road behavior of drivers of different sexes, due to their personal characteristics and existing stereotypes of perception of women-drivers in society. The article presents the results of empirical studies of the achievement motivation, inclination to risk and attitude to uncertainty of men and women drivers. Theoretical analysis suggests that male drivers are more risk-averse and more tolerant of uncertain situations in the traffic than women. At the same time, men in driving are more focused on achieving success, and women — on avoiding failure. An empirical study of 58 drivers (29 men and 29 women) showed that both men and women in driving demonstrate motivation for avoiding failure, however, the reasons for this are different. For women, they are linked to existing societal stereotypes about women’s ability to drive, and for men, to the complication of traffic and increased penalties for traffic violations. Male drivers risk appetite combined with a motive for avoiding failure. The study of attitudes to uncertainty revealed significant differences in the level of tolerance among drivers of different sexes, especially to the difficulty of the situation. We believe that women, due to existing stereotypes, perceive driving activity from the very beginning as more difficult than men, which also explains some of the features of their behavior on the road. The article presents recommendations on optimization of training in driving schools for drivers of different sexes

Physio‐climatic controls on vulnerability of watersheds to climate and land use change across the U. S.

AbstractUnderstanding how a watershed's physio‐climatic characteristics affect its vulnerability to environmental (climatic and land use) change is crucial for managing these complex systems. In this study, we combine the strengths of recently developed exploratory modeling frameworks and comparative hydrology to quantify the relationship between watershed's vulnerability and its physio‐climatic characteristics. We propose a definition of vulnerability that can be used by a diverse range of water system managers and is useful in the presence of large uncertainties in drivers of environmental change. This definition is related to adverse climate change and land use thresholds that are quantified using a recently developed exploratory modeling approach. In this way, we estimate the vulnerability of 69 watersheds in the United States to climate and land use change. We explore definitions of vulnerability that describe average or extreme flow conditions, as well as others that are relevant from the point of view of instream organisms. In order to understand the dominant controls on vulnerability, we correlate these indices with watershed's characteristics describing its topography, geology, drainage, climate, and land use. We find that mean annual flow is more vulnerable to reductions in precipitation in watersheds with lower average soil permeability, lower baseflow index, lower forest cover, higher topographical wetness index, and vice‐versa. Our results also indicate a potential mediation of climate change impacts by regional groundwater systems. By developing such relationships across a large range of watersheds, such information can potentially be used to assess the vulnerability of ungauged watersheds to uncertain environmental change.

Effectiveness of Grips in alleviating traffic congestion

Graphic route information panels (Grips) provide traffic information by displaying a few distinct colours on simple network topologies. The images that the Grip are able to present are usually limited, as are their corresponding splitting rates. Assessing the effectiveness of these limited splitting rates in alleviating traffic congestion is of practical significance. To this end, this paper considers the simple but typical Grip that presents three colours on a network with two routes. An optimal iterative guidance strategy is taken as the strategy for selecting splitting rates. All possible splitting rates that the Grip can deliver are enumerated in deterministic and stochastic simulation scenarios, in which drivers completely follow route guidance and subsequently choose a route based on a probability. The results are analysed and compared, and further general conclusions are made. A number of investigations are addressed including a basic requirement for the Grip's capacity to alleviate congestion, stability of the traffic influenced by the guidance information, impact of the uncertainty of drivers' understanding of the information, and so on. The results are useful in the assessment and proposal of practical route guidance strategies as well as when educating drivers to understand better the guidance information provided by the Grip.

Lane-changing model with dynamic consideration of driver's propensity

Lane-changing is the driver's selection result of the satisfaction degree in different lane driving conditions. There are many different factors influencing lane-changing behavior, such as diversity, randomicity and difficulty of measurement. So it is hard to accurately reflect the uncertainty of drivers' lane-changing behavior. As a result, the research of lane-changing models is behind that of car-following models. Driver's propensity is her/his emotion state or the corresponding preference of a decision or action toward the real objective traffic situations under the influence of various dynamic factors. It represents the psychological characteristics of the driver in the process of vehicle operation and movement. It is an important factor to influence lane-changing. In this paper, dynamic recognition of driver's propensity is considered during simulation based on its time-varying discipline and the analysis of the driver's psycho-physic characteristics. The Analytic Hierarchy Process (AHP) method is used to quantify the hierarchy of driver's dynamic lane-changing decision-making process, especially the influence of the propensity. The model is validated using real data. Test results show that the developed lane-changing model with the dynamic consideration of a driver's time-varying propensity and the AHP method are feasible and with improved accuracy.

Supply of carbon sequestration and biodiversity services from Australia's agricultural land under global change

Global agroecosystems can contribute to both climate change mitigation and biodiversity conservation, and market mechanisms provide a highly prospective means of achieving these outcomes. However, the ability of markets to motivate the supply of carbon sequestration and biodiversity services from agricultural land is uncertain, especially given the future changes in environmental, economic, and social drivers. We quantified the potential supply of these services from the intensive agricultural land of Australia from 2013 to 2050 under four global outlooks in response to a carbon price and biodiversity payment scheme. Each global outlook specified emissions pathways, climate, food demand, energy price, and carbon price modeled using the Global Integrated Assessment Model (GIAM). Using a simplified version of the Land Use Trade-Offs (LUTO) model, economic returns to agriculture, carbon plantings, and environmental plantings were calculated each year. The supply of carbon sequestration and biodiversity services was then quantified given potential land use change under each global outlook, and the sensitivity of the results to key parameters was assessed. We found that carbon supply curves were similar across global outlooks. Sharp increases in carbon sequestration supply occurred at carbon prices exceeding 50$tCO2−1 in 2015 and exceeding 65$tCO2−1 in 2050. Based on GIAM-modeled carbon prices, little carbon sequestration was expected at 2015 under any global outlook. However, at 2050 expected carbon supply under each outlook differed markedly, ranging from 0 to 189MtCO2yr−1. Biodiversity services of 3.32% of the maximum may be achieved in 2050 for a 1$B investment under median scenario settings. We conclude that a carbon market can motivate supply of substantial carbon sequestration but only modest amounts of biodiversity services from agricultural land. A complementary biodiversity payment can synergistically increase the supply of biodiversity services but will not provide much additional carbon sequestration. The results were sensitive to global drivers, especially the carbon price, and the domestic drivers of adoption hurdle rate and agricultural productivity. The results can inform the design of an effective national policy and institutional portfolio addressing the dual objectives of climate change and biodiversity conservation that is robust to future uncertainty in both national and global drivers.

Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms

Transportation electrification has become an important issue in recent decades and the large scale deployment of electric vehicles (EVs) has yet to be achieved. The smart coordination of EV demand addresses an improvement in the flexibility of power systems and reduces the costs of power system investment. The uncertainty in EV drivers’ behaviour is one of the main problems to solve to obtain an optimal integration of EVs into power systems. In this paper, an optimisation algorithm to coordinate the charging of EVs has been developed and implemented using a Genetic Algorithm (GA), where thermal line limits, the load on transformers, voltage limits and parking availability patterns are taken into account to establish an optimal load pattern for EV charging-based reliability. This methodology has been applied to an existing residential low-voltage system. The results indicate that a smart charging schedule for EVs leads to a flattening of the load profile, peak load shaving and the prevention of the aging of power system elements.

The Uncertainty of Drivers’ Gap Selection and its Impact on the Fundamental Diagram

Time gap, defined as the time difference between the departure of the leading vehicle and the arrival of the following at the designated test point, is of great importance for both microscopic and macroscopic traffic modeling. In this paper, we report our recent investigation on a large amount of time gap data. By categorizing time gaps according to traffic conditions (indicated by travel speeds) and driving conditions (including no-speed-change, acceleration, and deceleration), we first studied the uncertainty in drivers’ gap selection, which is determined by drivers’ perception of selecting a comfortable and safe distance to the front vehicle. As we found out, under congested conditions, the average time gaps selected by drivers display little variation when drivers do not accelerate or decelerate; but when drivers are accelerating or decelerating, on average, they choose different time gaps with different speeds.We further studied the impact of the uncertainty of drivers’ gap selection on the macroscopic traffic variables by considering the road traffic as a stochastic process. We derived a shifted Gamma traffic count distribution to describe the randomness of traffic flow caused by the uncertainty of drivers’ gap selection, and then derived the fundamental diagram (FD) based on the count distribution. We found out that the uncertainty of drivers’ gap selection partially contributes to the scatter in the FD; and the shape of the FD changes with driving conditions: the right-hand-side of the FD most likely is a linear line for no-speed-change condition; but it becomes a convex curve when vehicles are accelerating and a concave curve when decelerating. The different capacity values shown in the FD offer a possible explanation for the “capacity drop”.

On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model

Abstract. Phenology, the timing of recurring life cycle events, controls numerous land surface feedbacks to the climate system through the regulation of exchanges of carbon, water and energy between the biosphere and atmosphere. Terrestrial biosphere models, however, are known to have systematic errors in the simulation of spring phenology, which potentially could propagate to uncertainty in modeled responses to future climate change. Here, we used the Harvard Forest phenology record to investigate and characterize sources of uncertainty in predicting phenology, and the subsequent impacts on model forecasts of carbon and water cycling. Using a model-data fusion approach, we combined information from 20 yr of phenological observations of 11 North American woody species, with 12 leaf bud-burst models that varied in complexity. Akaike's Information Criterion indicated support for spring warming models with photoperiod limitations and, to a lesser extent, models that included chilling requirements. We assessed three different sources of uncertainty in phenological forecasts: parameter uncertainty, model uncertainty, and driver uncertainty. The latter was characterized running the models to 2099 using 2 different IPCC climate scenarios (A1fi vs. B1, i.e. high CO2 emissions vs. low CO2 emissions scenario). Parameter uncertainty was the smallest (average 95% Confidence Interval – CI: 2.4 days century−1 for scenario B1 and 4.5 days century−1 for A1fi), whereas driver uncertainty was the largest (up to 8.4 days century−1 in the simulated trends). The uncertainty related to model structure is also large and the predicted bud-burst trends as well as the shape of the smoothed projections varied among models (±7.7 days century−1 for A1fi, ±3.6 days century−1 for B1). The forecast sensitivity of bud-burst to temperature (i.e. days bud-burst advanced per degree of warming) varied between 2.2 days °C−1 and 5.2 days °C−1 depending on model structure. We quantified the impact of uncertainties in bud-burst forecasts on simulated photosynthetic CO2 uptake and evapotranspiration (ET) using a process-based terrestrial biosphere model. Uncertainty in phenology model structure led to uncertainty in the description of forest seasonality, which accumulated to uncertainty in annual model estimates of gross primary productivity (GPP) and ET of 9.6% and 2.9%, respectively. A sensitivity analysis shows that a variation of ±10 days in bud-burst dates led to a variation of ±5.0% for annual GPP and about ±2.0% for ET. For phenology models, differences among future climate scenarios (i.e. driver) represent the largest source of uncertainty, followed by uncertainties related to model structure, and finally, related to model parameterization. The uncertainties we have quantified will affect the description of the seasonality of ecosystem processes and in particular the simulation of carbon uptake by forest ecosystems, with a larger impact of uncertainties related to phenology model structure, followed by uncertainties related to phenological model parameterization.

Analysis and synthesis of robust control for linear systems of double damping

The paper sets forth the method for constructing a robust control suspension of the automobile when there is an interval uncertainty of car cargo weight and drivers’ mass as well as the impossibility of measuring deviations of the car body and the driver's seat of their state of rest. The paper gives clear justification for the relevance of the application of synthesized robust control in comparison with other on the quality of the transient processes.

첨두 및 비첨두시 VMS 교통정보의 가치 변화 연구

ITS사업을 통한 교통정보제공편익은 크게 두 가지 측면으로 볼 수 있는데, 공공측면의 경우 교통정보를 교통관리의 수단으로 활용하였을 때 나타나는 통행시간 절감 편익이나, ITS사업의 경제적 타당성 평가시 교통정보제공편익은 독립적인 편익으로 인정받지 못하고 첨단교통관리(ATMS)의 부산적 편익으로 간주되어 왔다. 이용자 측면에서는 전방 교통정보 제공에 따른 운전자의 불확실성 감소이다. 운전자는 교통정보를 통해 통행의 불확실성이 감소한다고 판단할 경우 이에 대해 지불의사를 가지므로 이를 근거로 교통정보의 가치를 추정할 수 있다. 최근 교통정보의 편익을 계량화하려는 노력은 많아지고 있으나, 교통정보가치의 다변성과 정보 자체의 속성을 고려하지 못하고 단일 값으로 제시하고 있어 교통정보제공편익을 과대추정하거나 왜곡할 우려가 있다. 따라서 합리적인 교통정보제공편익을 산정하기 위해서는 교통정보 가치의 세분화가 필요하다. 이에 본 연구에서는 VMS가 제공하는 교통정보의 가치를 추정하되 통행시간대(첨두/비첨두)에 따른 세분화를 통하여 보다 합리적으로 교통정보의 가치를 산정하고, 추정결과에 대한 가설검정을 통해 가치 세분화의 통계적 유의성을 밝히고자 한다. The benefit of traffic information on variable message sign can be divided into two. At the public level, the benefit of ATIS is the travel time saving, which is not only induced from ATIS, but also mixed with that of ATMS. In the economic appraisal of ITS, the benefit of ATIS has so far been regarded as the derived benefit from ATMS. At the user level, the benefit of ATIS is reduced driver uncertainty through the forward traffic status information. User can benefit from the information on VMS and therefore may have the willingness to pay for it. Recently attempt to qualify the value of information on VMS was increased, but there was a danger of distorting or over-estimates of the ATIS benefit because the related studies didn't consider the time-dependent attributes of traffic information and provided the single value. Estimates of the time-varying value should be needed for a rigorous economic appraisal of ATIS. In this study, we varied the value of information on VMS according to peak and non peak trip and verified the hypothesis that time-varying of value was statistically significant.

Uncertainty in predictions of forest carbon dynamics: separating driver error from model error

We present an analysis of the relative magnitude and contribution of parameter and driver uncertainty to the confidence intervals on estimates of net carbon fluxes. Model parameters may be difficult or impractical to measure, while driver fields are rarely complete, with data gaps due to sensor failure and sparse observational networks. Parameters are generally derived through some optimization method, while driver fields may be interpolated from available data sources. For this study, we used data from a young ponderosa pine stand at Metolius, Central Oregon, and a simple daily model of coupled carbon and water fluxes (DALEC). An ensemble of acceptable parameterizations was generated using an ensemble Kalman filter and eddy covariance measurements of net C exchange. Geostatistical simulations generated an ensemble of meteorological driving variables for the site, consistent with the spatiotemporal autocorrelations inherent in the observational data from 13 local weather stations. Simulated meteorological data were propagated through the model to derive the uncertainty on the CO2 flux resultant from driver uncertainty typical of spatially extensive modeling studies. Furthermore, the model uncertainty was partitioned between temperature and precipitation. With at least one meteorological station within 25 km of the study site, driver uncertainty was relatively small ( 10% of the total net flux), while parameterization uncertainty was larger, 50% of the total net flux. The largest source of driver uncertainty was due to temperature (8% of the total flux). The combined effect of parameter and driver uncertainty was 57% of the total net flux. However, when the nearest meteorological station was > 100 km from the study site, uncertainty in net ecosystem exchange (NEE) predictions introduced by meteorological drivers increased by 88%. Precipitation estimates were a larger source of bias in NEE estimates than were temperature estimates, although the biases partly compensated for each other. The time scales on which precipitation errors occurred in the simulations were shorter than the temporal scales over which drought developed in the model, so drought events were reasonably simulated. The approach outlined here provides a means to assess the uncertainty and bias introduced by meteorological drivers in regional-scale ecological forecasting.

Strategic foresight in corporate organizations: Handling the effect and response uncertainty of technology and social drivers of change

Today many companies in many industries put a lot of efforts into monitoring and investigating emerging drivers of change in their business environment, particularly in relation to new technologies and events in the social, economic, political and ecological landscapes which surround their industry. So far scholars in the literature on foresight and future studies focused on the techniques and practices for analysing the “state” uncertainty about the likely path of evolution of emerging drivers of change. Anyway, less attention was devoted to the “effect” uncertainty about the impact of drivers of change on the competitive position of the firm and to the “response” uncertainty about how to take advantage of these drivers. These are the main issues we take into account, through a field research on firms that, notwithstanding they were able to properly assess the likely evolution (state uncertainty) of relevant drivers of change in their industry, either were successful or dramatically failed in handling the effect and response uncertainty of these drivers. Moreover, we carried out multiple case studies of some large companies that have established an organizational unit dedicated to strategic foresight. Overall, the results of our research may contribute to improve the effectiveness of strategic foresight and to increase its value added to the planning process of corporate firms, while providing helpful insight to public organizations that promote foresight exercises for enhancing the competitiveness of local firms.

Determinants of Route Choice and Value of Traveler Information

Drivers receive value from traveler information in several ways, including the ability to save time, but perhaps more important is the value of certainty as it affects other personal, social, safety, or psychological factors. This information can be economically valued. The benefit of reduction in driver uncertainty when information is provided at the beginning of the trip is the main variable measured in this research. User preferences for routes were assessed as a function of the presence and accuracy of information while controlling for other trip and route attributes. Data were collected in a field experiment in which 113 drivers, given real-time travel time information with varying degrees of accuracy, drove four alternative routes between a preselected origin-destination pair in the Twin Cities, Minnesota, metropolitan area. Ordinary regression, multinomial, and rank-ordered logit models produced estimates of the value of information with some variation. Results showed that travelers were willing to pay up to $1 per trip for pretrip travel-time information. The value of information is higher for commute and event trips and when congestion on the usual route is heavier. The accuracy of the traveler information was also a crucial factor. Travelers will not pay for information unless they perceive it to be accurate. Most travelers (70%) prefer that such information be provided free by the public sector, whereas some (19%) believe that it is better for the private sector to provide such service at a charge.