Ata M. Khan

Avoiding highway infrastructure construction cost overruns and reducing associated claims and disputes continues to be a challenge in many countries. Research is needed in identifying notable project planning and management deficiencies that are likely to cause cost overruns. The literature suggests numerous potential causes of cost overrun but the clustering of cause variables and relative importance of clusters has not been researched. The research reported here addresses this knowledge gap using predictive models developed with data contributed by several agencies in participating countries and suggests mitigation measures. Following a review of methods and data sources, a methodological framework is advanced that encompasses statistical methods well suited for providing a scientific basis for identifying important clusters of cost overrun variables. Fifty-three completed questionnaires contributed by knowledge experts and experienced managers from Canada, the United States, the Middle East, and Australia met the sample requirements of statistical methods. Starting from 53 variables, the principal component-supported factor analysis method identified clusters of cost overrun variables and their relative importance was inferred with developed logistic regression models. Deeper insights into the causes of cost overruns obtained from this research suggest mitigation measures (e.g., improved qualification and experience of personnel, enhanced planning and design practices, risk analysis of inputs to cost estimation process) that are within reach of managers. The results can enhance infrastructure planning and management practice including a reduction in claims and disputes.

The air transportation industry is challenged to address airport curbside delay problems that affect landside service quality and can potentially impact check-in operations. Methodological advances guided by industry requirements are needed to support curbside improvement studies. Existing methods require verification of assumptions prior to application or need expensive surveys to acquire data for use in microsimulations. A probability-based macrosimulation method is advanced for the evaluation of the level of service and capacity of the curbside processor. A key component of the method is the simulation of the stochastic balance of demand and available curb space for unloading/loading tasks using the Monte Carlo simulation model. The method meets the planning and operation requirements with the ability to analyze conditions commonly experienced at the curb area. Example applications illustrate the flexibility of the method in evaluating existing as well as planned facilities of diverse designs and sizes. The developed method can contribute to curbside processor delay reduction and due to the macroscopic nature of the method, the data requirements can be met by an airport authority without costly surveys.

Electric Level 4 connected automated vehicles (CAVs) are now allowed to demonstrate their automation capability in shared mobility robotaxi and microtransit services in geofenced areas in several cities around the world. Private and public sector stake-holders need predictions of their adoption without regulatory constraints for personal mobility and use in shared mobility services. In anticipation of the future presence of CAVs in transportation vehicle fleets, governments are planning necessary regulatory and infrastructure changes. Accompanying this need for forecasts is the acknowledgement that CAV adoption decisions must be made under uncertain states of technology and infrastructure readiness. This paper presents a Bayesian predictive modelling framework for electric Level 4 CAV adoption in the 2030–2035 application context. The inputs to the Bayesian model are obtained from effectiveness estimates of CAV applications that are processed with the Monte Carlo method to account for uncertainties in these estimates. Scenarios of CAV adoption in the 2030–2035 period are analyzed using the Bayesian model, including the quantification of the value of new information obtainable from demonstration studies intended to reduce uncertainties in technology and infrastructure readiness. The results show that in the 2030–2035 application context, the CAVs are likely to be adopted, provided that the trajectory of progress in technology and infrastructure readiness continues, and potential adopters are offered opportunities to learn about Level 4 CAV technological capabilities in a real life service environment. The threshold level of the probability of adoption enhances significantly with high-reliability demonstration results that can reduce uncertainties in adoption decisions. The findings of this research can be used by private and public sector interest groups.

: Air transportation industry is challenged to address airport curbside delay problem that affects landside service quality and can potentially impact check-in operation. Methodological advances guided by industry requirements are needed to support curbside improvement studies. Existing methods require verification of assumptions prior to application or need expensive surveys to acquire data for use in microsimulations. A probability-based macrosimulation method is advanced for the evaluation of level of service and capacity of the curbside processor. Following problem definition and literature review, the theoretical foundation of the method is described as the simulation of the stochastic balance of demand and available curb space for unloading/loading task. The method meets the planning and operation requirements with ability to analyse conditions commonly experienced at the curb area. Example applications illustrate the flexibility of the method in evaluating existing as well as planned facilities of diverse designs and sizes. The developed method can contribute to curbside processor delay reduction and due to the macroscopic nature of the method, the data requirements can be met by an airport authority without costly surveys.

The electric Level 4 connected automated vehicles (CAVs) are now allowed in several cities around the world to demonstrate their automation capability in shared mobility robotaxi and microtransit services in geofenced areas. Private and public sector stake-holders need predictions of their adoption without regulatory constraints for personal mobility and use in shared mobility services. In anticipation of the future presence of CAVs in transportation vehicle fleets, governments are planning necessary regulatory and infrastructure changes. Accompanied with the need for forecasts is the acknowledgement that CAV adoption decisions must be made under uncertain states of technology and infrastructure readiness. This paper presents a Bayesian predictive modelling framework for electric Level 4 CAV adoption in the 2030-2035 application context. The inputs to the Bayesian model are obtained from effectiveness estimates of CAV applications that are processed with the Montecarlo method to accounts for uncertainties in these estimates. Scenarios of CAV adoption in the 2030-2035 period are analyzed using the Bayesian model, including the quantification of the value of new information obtainable from demonstration studies intended to reduce uncertainties in technology and infrastructure readiness. The results show that in the 2030-2035 application context, the CAVs are likely to be adopted, provided that the trajectory of progress in technology and infrastructure readiness continues, and potential adopters are offered opportunities to learn about Level 4 CAV technological capabilities in real life service environment. The findings of this research can be used by private and public sector interest groups.

Assessing risk in life-cycle cost and benefit estimates of highway investments is recommended by major organizations such as the World Bank and the U.S. Federal Highway Administration. This challenging task needs methodological support. Mutually exclusive investment alternatives can differ in terms of the costs of construction, maintenance, rehabilitation, and end-of-life value. Due to many causal factors and the long life of highway infrastructure, these items cannot be estimated with certainty. To go beyond the study of the sources of uncertainty, a method is needed to check the economic feasibility of acquiring additional information for deeper insight. This paper reports on research on the value of Bayesian pre-posterior information for refining the life-cycle cost analysis of uncertain costs and benefits for evaluating highway investment alternatives. Example applications demonstrate how the Bayesian pre-posterior analysis can be applied to check the feasibility of obtaining new information for enhancing the life-cycle cost analysis of highway investments. The value of Bayesian pre-posterior information is illustrated for reducing risk. Also, depending upon the specifics of uncertain states, a change in the choice of the investment alternative for implementation can be investigated. The product of this research can potentially upgrade highway infrastructure planning and management practices.

Assessing risk in life cycle cost and benefit estimates of highway investments is recommended by major organizations such as the World Bank and the U.S. Federal Highway Administration. This challenging task needs methodological support. Mutually exclusive investment alternatives can differ in terms of costs of construction, maintenance, rehabilitation, and end of life value. Due to many causal factors and the long life of highway infrastructure, these items cannot be estimated with certainty. To go beyond sources of uncertainty study, a method is needed to check economic feasibility of acquiring additional information for deeper insight. This paper reports research on the value of Bayesian pre-posterior information for refining life cycle analysis of uncertain costs and benefits for evaluating highway investment alternatives. Example applications demonstrate how the Bayesian pre-posterior analysis can be applied to check the feasibility of obtaining new information for enhancing life cycle analysis of highway investments. The value of Bayesian pre-posterior information is illustrated for reducing risk. Also, depending upon specifics of uncertain states, a change in the choice of the investment alternative for implementation can be investigated. The product of this research can potentially upgrade highway infrastructure planning and management practice.

Electric-connected automated vehicle (CAV) shuttles, as a part of the sustainable microtransit system, have the potential to fill public transit service gaps. Following technology and traveler acceptance tests that are underway around the world, mass-produced CAVs will be considered for shared mobility service, including “first/last mile” travel between public transit hub stations and medical campuses or other activity centres. Thus, there is a need for increased knowledge on treating risk in such applications. This paper covers the planning and economic feasibility of an advanced technology level 4 automated vehicle-based microtransit system, considering uncertain service and economic feasibility factors. The methods used are advanced for addressing uncertainties in travel demand, service factors, and the economic feasibility of investments by public and private sector entities. Specifically, a probability-based macro simulation approach is used to treat demand and supply-side service factors as stochastic, and it is adapted for risk analysis in financial decision-making. The effects of uncertain life-cycle costs on fares and the rate-of-return are described. Results are favourable regarding the technical and economic feasibility of advanced technology-based microtransit first/last mile service. The findings reported here are a contribution to knowledge on the feasibility of implementing CAV-based first/last mile, and other microtransit services, under uncertainty.

Electric connected automated vehicle (CAV) shuttles as a part of sustainable microtransit system have the potential to fill public transit service gaps. Following technology and traveler acceptance tests that are underway around the world, mass produced CAVs will be considered for shared mobility service, including “first/last mile” travel between public transit hub stations and medical campuses or other activity centers. There is a need for knowledge on treating risk in such applications. This paper covers planning and economic feasibility of advanced technology level 4 automated vehicle-based microtransit system considering uncertain service and economic feasibility factors. Methods are advanced for addressing uncertainties in travel demand, service factors, and economic feasibility of investments by public and private sector entities. Specifically, a probability-based macro simulation approach is used to treat demand and supply-side service factors as stochastic and is adapted for risk analysis in financial decision making. The effects of uncertain life cycle costs on fares and rate of return are described. Results are favorable regarding the technical and economic feasibility of advanced technology-based microtransit first/last mile service. The findings reported here are a contribution to knowledge on feasibility of implementing CAV-based first/last mile and other microtransit services under uncertainty.