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Sustainable transport policies are fundamental to adapt transport capacity to existing and future travel demand. In this context, this study aims to develop a System Dynamics (SD), to verify the effects of these policies, focusing on congestion and air pollution. Combined with the SD, the discrete choice utility approach was used to predict the modal share in different policy spaces. In addition, it is carried out a case study in Rio de Janeiro in two realities: pre- and post-pandemic. The results show how mitigation policies can reduce transport externalities (congestion and pollution). The encouragement of high-capacity public transport and car ownership control are the best measures, obtaining high simulation scores. The post-pandemic scenario shows that reducing travel demand is the key to achieving better results. All scores obtained in this scenario are better than in pre-pandemic scenario. Finally, results point out that ride-hailing should be used in a conscious way. [ABSTRACT FROM AUTHOR]

The safety of bicycle infrastructure is a primary factor influencing bicycle travel. While cyclists' perspectives on infrastructure safety are extensively studied, they are merely the end users. Decisions on infrastructure design are made by engineers and urban development specialists. Therefore, it is crucial to determine if these professionals' safety assessments align with those of cyclists. A qualitative survey was conducted with 5 expert engineers and 5 urban development specialists, each having 5 to 20 years of experience in transportation infrastructure planning. Kendall's coefficient of concordance W was used to assess the compatibility of their opinions. The results showed significant compatibility: W = 0.697 for engineers and W = 0.511 for urban development specialists. Seventeen cycling infrastructure installation schemes were evaluated. Both engineers (M = 10.0, SD = 0.0) a nd u rban d evelopment specialists (M = 9.8, SD = 0.44) indicated the DT_2 option as providing the greatest sense of security, where the bicycle path is physically separated from both the carriageway and pedestrian path. The key findings reveal agreement on the safety of straight-street segments of bicycle infrastructure but diverging opinions at intersections zones. Urban development specialists are influenced by existing practices and legal frameworks lacking detailed cycling infrastructure guidelines at intersections. Engineers align more closely with cyclists' perceptions, emphasising physical separation and speed reduction measures. The study concludes that urban development specialists need to better understand cyclists' needs and prioritize safer infrastructure solutions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study presents the computational architecture of the integrated transport, land-use, and emission (iTLE) modeling system. It incorporates the effects of physical-virtual activity-space interactions within the microsimulation framework, making it a comprehensive tool for predicting travel demand and evaluating the impacts of evolving activity-travel behavior on transport and land-use systems. The model systematically integrates households' and individuals' long-term, medium-term, and short-term travel related decisions, and traffic assignment allowing for more reliable estimates of travel demand. Application of the iTLE prototype in the Halifax Regional Municipality (HRM), Canada reveals notable trends, including a reduction in travel time for work activities, highlighting the potential of teleworking as a pragmatic strategy for traffic management. The model's flexibility is demonstrated through its ability to capture the trade-offs between physical and virtual activity environments, aligning with the intricacies of modern digitalized societies. Integration of iTLE with the traffic assignment system enhances its applicability across diverse urban contexts, streamlining the process of setting origin-destination trip matrices and significantly improving computational efficiency. By systematically coupling the micro-behavioral decision processes, iTLE generates robust estimates of traffic flow, emissions, and energy use, providing reliable insights for sustainable and efficient transportation planning. This study contributes to advancing integrated urban modeling, offering a valuable tool that can be replicable for multiple cities in Canada and beyond. [ABSTRACT FROM AUTHOR]

Station-pair passenger flow forecast modeling is crucial for public transportation to address emerging needs. The accurate prediction and estimation will provide backbone support for various features of transport, viz. (a) Demand forecasting for resource allocation during peak travel periods; (b) Service planning to determine transportation frequency on specific routes; (c) Resource allocation for vehicles and capacity adjustments, including planning for new fleet size; (d) Infrastructure planning to identify areas requiring additional development; (e) Enhancing passenger experience with ample seating and amenities; and (f) Emergency planning for crowd control and passenger safety. Recent advancements in transit information station-pair estimation methods can be implemented with Data Science. Over the years, there has been significant progress in developing innovative modeling approaches to estimate travel behaviour, taking into account various factors such as timeslots, holidays, and other relevant features. In this study, we have synthesized ticket data from Thane Municipal Transport (TMT), collected from e-ticket machines, spanning from January 1 to June 23, 2023, covering all routes and comprising more than 2.80 crore tickets. This paper presents a comprehensive station-pair short-term passenger flow forecast by utilizing three boosting models: extreme Gradient Boosting (XGBoost), Light Gradient Boosting model (LightGBM), and Categorical Boosting model (CatBoost). Their implementation is well-suited based on statistical parameters. The study concludes by comparing the results of the models, considering computation time and statistical parameters, to validate their superiority. By looking into day to day requirements, the LightGBM model is most appropriate for implementation with an accurate result. The research findings contribute to providing valuable data support and a robust scientific foundation for managing passenger flow through route planning and dispatching in urban transit systems. [ABSTRACT FROM AUTHOR]

A historic urban district is a quarter built many years ago and contains valuable memories over a limited area with narrow streets for pedestrians, primitive transportation, and outdoor activities. Over the years, many districts have been growing randomly, and motorized transport has enlarged significantly and created severe problems. Improving the situation needs heritage protection and sustainable mobility development on its streets. The desired mobility focuses on managing travel demands, accessibility for all street users, and enhancing the environment. This research presents a proposed approach with an application framework for sustainable mobility development on historic roads according to its current conditions at the strategic level. It includes sequential phases to define desired goals, formulate and evaluate planning scenarios containing different action measures based on sustainability indicators and application of micro-simulation. In case of inappropriate evaluation results, approach phases allow the modification and re-evaluation of additional measures until deciding an acceptable solution. The application is limited to Ras El-Teen Palace Street, prominent in the ancient El Gomrok district, Alexandria and restricted to a Do-Nothing solution and a planning scenario. The application proves the approach's practicality and feasibility by improving traffic behaviour. Furthermore, the planning approach can indicate technical recommendations for further improvement. [ABSTRACT FROM AUTHOR]

The article focuses on the deployment of the Next Generation Transit Signal Priority (TSP) system in Portland, Oregon, as part of a project to enhance transit efficiency and reliability along the Division Street corridor. The main topics covered are the goals of the project to improve transit and reduce injuries, the implementation of the Next Generation TSP system and the results showing improved transit performance and the lessons learned from the project's implementation.

This paper develops a multivariate spatial model for the joint analysis of daytime and nighttime crash frequencies by injury severity in traffic analysis zones (TAZs). The model specification allows for spatial correlation across TAZs, heterogeneous effects specific to crash time and severity, and correlations across response types under a Bayesian multivariate conditional autoregressive framework. One hundred and thirty one TAZs in Hong Kong, China, with traffic crash, traffic flow, roadway network, and land use data for a one-year period are selected to calibrate the advocated model. Considerable spatial and heterogeneous effects are found for each type crash frequency. Significant correlations exist in the heterogeneous effects for various severity levels and those for daytime and nighttime. The Bayesian estimates of the regression coefficients reveal that there are significant inconsistencies in the set of factors contributing to zonal daytime and nighttime crashes at various severity levels. [ABSTRACT FROM AUTHOR]

The article provides information about the use of dynamic speed feedback signs (DSFS) as a potential solution to improve safety on freeway exit ramps. It demonstrates how these signs detect the speed of approaching vehicles using technology like LiDAR and display it. It provides details about the characteristics of the signs and the messages displayed based on the approaching vehicle's speed.

This research aimed to address a problem of the Transit-NDP with an upper level that optimizes the vehicles' Network Travel Time (NTT) subject to a budget constraint, and the lower level estimates vehicles' NTT based on a travel demand model (TDM), which involves identifying the optimal transit projects for reducing the NTT. The study objective is to explore the impact of a naïve model of the more complex Transit-NDP, where stakeholders suggest a group of potential transit projects. A complete evaluation of the enumeration and sampling search strategy is conducted for the two test networks. A sensitivity analysis is conducted on the impact of a demand increase for each test network. The Halle Network exhibits an almost constant NTT reduction ranging between 47.64% and 50.79% while the Karlsruhe Network exhibits a different behaviour with an NTT reduction ranging between 39.70% and 52.40%. In both networks, a set of candidate transit projects were members of the best solution for all test runs. The sensitivity analysis demonstrated that some candidate transit projects selected at lower budget levels, demand levels, or both were not necessarily selected as the best solutions at upper budget, demand levels, or both. In addition, the resulting dataset of the enumeration and sampling was utilized to develop a Random Forest regression model that produces estimates of the NTT with a success rate of 90%. [ABSTRACT FROM AUTHOR]

The inclusion of human behavioral factors in travel demand analysis is a new trend in transportation planning. Perceived security (PS) is one of the crucial behavioral factors governing urban travelers' choices and demand analysis, exclusively for active modes of transportation like bicycles and vulnerable road users like women. However, no scientific attempt has been made to explain and quantify PS for women cyclists. We estimate women cyclists' PS by considering individual, social, and built environment features from the data collected in Tehran, Iran. Fifty-two women were recruited as the test participants in a VR-based bicycle experimental system (VRBES). For analysis, we focused on the tree-based machine learning (ML) methods besides an econometric model. In the end, the random forest (RF) algorithm is chosen and improved for the best performance (with test-R2=0.69) on the dataset, and feature importance showed population, a social feature, is the most important. [ABSTRACT FROM AUTHOR]

In the cost–benefit analysis of transportation investment, although the benefit estimation method due to the effect of travel time saving has been widely used, several limitations with the method have been pointed out in a number of studies. As an alternative, a logsum-based benefit calculation has been widely adopted, with most of the research focused on urban transportation. Therefore, it is rarely applied to interregional transportation. In this study, we applied a logsum-based approach to the calculation of benefits for expressway road projects in Korea's long-term transportation plan. To calculate the logsum, this study used input data from both the mode choice and destination choice models, finding that an analysis of the feasibility of inter-regional road investment was possible. Various studies have calculated the benefits based on one of the two models. The two methods have similar formulas; however, varying results were derived due to differences in the application of formulas for parameter estimation and benefit estimation. In conclusion, the proposed methods are expected to enable a more comprehensive evaluation of the transport system. Looking at more detailed research results, the logsum based on the mode choice model had great benefits in the Seoul metropolitan area where demand was high; however, the logsum of the destination-based model showed a large difference in distribution of benefits evenly by various metropolitan areas. On the other hand, it was found that the traditional reduction of travel time was much less by region than the logsum methods, but the effect was large in the metropolitan area. From the perspective of the theoretical and practical consumer surplus analysis, we suggest open discussions regarding which method to choose when calculating benefits from the mode choice logsum model, the destination logsum model and travel time savings method. [ABSTRACT FROM AUTHOR]

The article focuses on the Transportation Planner Initiative Action Plan developed by Institute of Transportation Engineers International (ITE) Board of Direction to broaden community of transportation professionals and diversify membership by identifying opportunities to enhance ITE's services.

In order to properly planning transportation systems and providing adequate services, it is essential to characterize the passenger demands, understand the behavior patterns and associate them to the routes considering space and time. Currently, promising alternatives based on the Internet of Things concepts allow adequately estimate the demand and provide very useful information. However, these devices are not widespread and usually, single GPS systems are more frequently available to monitor transportation systems worldwide. Conventional data analysis infrastructures commonly require great computational efforts to run the huge amount of data usually provided by GPS systems to get statistical and visual reports. In this quest for continuous improvement, new analysis or visualization infrastructures may require a highly repetitive processing algorithm applied in all data. This way of acting hinders the cognitive process, the capacity for analysis, and the inference of relevant information. In this context, this paper proposes an approach based on visual analytics for inferring passenger demand from GPS data. The proposed approach combines two stages. In the first stage, a space-time algorithm extracts indicators from the GPS data and makes them available. The second stage, an iterative visualization interface containing configurable filters and statistical functions, helps the user to explore intuitively the relationship between indicators and passenger demand. The case studies are based on actual data collected in the Living Lab for Electrical Mobility at University of Campinas. Results show that the proposed approach is promising and the inferred demand can be used to propose new routes and schedules for the buses at the University. [ABSTRACT FROM AUTHOR]

There are plenty of good transport plans for major cities, but few that are implemented and fewer still that have been transformational. At a time when planning imperatives are less about growth and more about adaptation to climate change, there is value in looking again at comprehensive approaches to transport development at a city-region level. Rarely is there a possibility of first-hand accounts of how a city that had failed to implement earlier (1970s) plans found a successful way forward twenty years later through an emphasis on the process of planning. With the benefit of being able to observe implementation of the EU-funded Dublin Transportation Initiative (DTI) over a twenty-five year period, the authors draw on their first-hand knowledge of how the DTI was created in the early 1990s, overcoming what had been missing in earlier planning attempts, and how an insistence on confronting key policy choices was crucial. The detail in the comprehensive plan certainly matters, but it’s the process by which the DTI was created and then carried forward, establishing a broad support-base, that determined its successful implementation. [ABSTRACT FROM AUTHOR]

Freight transport is crucial in fostering economic growth and enhancing societal well-being, but it also poses challenges for traffic management and environmental sustainability. For freight planning and policy formulation, it is important to measure and evaluate regional freight accessibility. This paper proposes a novel framework for analyzing regional freight accessibility based on truck trajectory data. The framework is structured around two principal components: extracting freight trip information and constructing a freight accessibility model. It introduces precise and effective methodologies for identifying truck parking zones based on trajectory data, thereby facilitating the extraction of complete freight trip information. Afterward, the framework integrates a distance-weighted topological analysis with multiple relevant indicators to comprehensively assess the freight accessibility of a region. Additionally, it incorporates spatial lag modeling to examine the factors influencing the spatial distribution of freight accessibility within a region. Applied to a case study in Hunan Province, China, the framework demonstrates its efficacy. The analysis reveals that accessibility in Changsha and two other cities stands out significantly higher than in other cities. The study offers valuable insights for strategic freight planning and policy formulation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Dalian University of Technology / Dalian Ligong Daxue Xuebao is the property of Journal of Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The long-distance cold chain transportation of fresh products from the place of origin to sale is costly due to the high probability of spoilage and high carbon emissions. The potential factors for the high cost of long-distance cold chain transportation are the spoilage of fresh goods caused by transportation vibration and the carbon emission caused by refrigerant use. How to minimize the impact of these factors through optimal transportation route planning is the current problem to be solved. Firstly, a longdistance cold chain logistics route planning model including goods spoilage cost and carbon emission cost is developed, considering the goods spoilage cost caused by transportation vibration and the carbon emission cost generated by refrigerant as well as several other factors. Secondly, an improved ant colony algorithm is used to solve the optimal solution of the model, and the algorithm is redesigned for the characteristics of the model. Thirdly, the pheromone update mechanism in the ant colony algorithm is improved by adding pheromone coefficients to accelerate the convergence speed of the algorithm. Finally, the validity of the model is verified by an arithmetic example. The model and method proposed in this paper help to reduce the cost in the process of long-distance cold chain transportation and bring practical application value. [ABSTRACT FROM AUTHOR]

The article reports that District of Columbia receives an annual apportionment of federal funds for transportation, and responsible for carrying out a statewide transportation planning process under the Statewide and Nonmetropolitan Transportation Planning rule.

The article highlight the numerous products, resources, and collaboration opportunities Institute of Transportation Engineers (ITE) has to offer planners at all stages of their careers being a transportation planner with efficient movement of people and goods on transportation systems.

Accurately estimating the number of people is a useful information to monitor the occupancy level of spaces due to the COVID-19 pandemic in order to keep social distancing. Automated counters are responsible for accurately counting people movement. This paper proposes a cost-effective automatic counting system based on a microcontroller through an infrared sensor integration to monitor occupancy of indoor spaces. In order to evaluate the proposed system in real-world scenarios, experiments were carried out at the main campus of the University of Campinas—Brazil. For the first case, two prototypes were installed at both the front and rear door of a bus in order to count the number of passengers boarding and alighting on the vehicle during its operation. In the second case, three prototypes were installed in the university restaurant ticket gates in order to count the number of people entering during lunchtime. Experimental results showed an accuracy of 91.45% and 98.65% for Cases 1 and 2, respectively. The results are promising, showing that the device has potential to be used for different proposes, such as controlling access to public and indoor spaces, public transportation planning, occupancy monitoring, and security system. [ABSTRACT FROM AUTHOR]

The article informs about the Thrive 2050 transportation analysis first evaluates how external factors such as climate change and technological change that can impact the county's transportation future, absent any new policy interventions to help the county achieve its transportation vision.

Continuous Approximation (CA) models have been used to solve transportation problems due to their ability to efficiently model complex transportation systems and offer flexibility in testing alternative policies. The current study develops a CA model for optimizing transportation planning for ferry services. The model is tested in a case study for a ferry network in the Aegean Sea, Greece. The specific network is chosen due to the high seasonal demand changes that the network experiences. For this reason, three strategies and three scenarios for each strategy are modeled, resulting in nine cases, to test the developed method. The three strategies reflect fleet size and ferry trip variations, while the three scenarios reflect variations in demand for each quarter of the year. The analysis shows that the estimated impacts provide an understanding of the relationship between time delay and operational costs, and the trade-offs between different strategies. [ABSTRACT FROM AUTHOR]

The article focuses on Maryland's Montgomery County approach to updating its road design standards using a Complete Streets approach, emphasizing safety and multimodal travel, including Complete Streets Design Guide to assist in achieving its goal of zero traffic-related deaths.

The article focuses on Washington DC's moveDC transportation plan that prioritizes safety, equity, mobility, project delivery, sustainability, and enjoyable spaces with a target of 75 percent non-auto commuter trips by residents, including strategies such as Bus Priority Program.

The article focuses on the 14 Street Transit and Truck Priority Pilot Project, which restricted through traffic on a Manhattan road, New York and was evaluated by team of transportation planners and engineers led by Sam Schwartz, resulting in decreased bus travel times, and lower crash rates.

Departure time choice is a key component of travel behavior that directly influences the spatial and temporal distribution of travel demand. This paper tries to develop a modeling framework for choosing the departure time that minimizes travel costs. In this regard, a modeling framework for generating departure time recommendations is proposed and applied to real commuting trips. The methodology is an extension of the departure time choice model with unreliable travel time. Two cases are considered. The first calculates the optimal time of departure when the mean of the travel time varies by time of day but the variance is constant. An exact solution to the departure time choice problem is provided for this case. In the second case, both the mean and variance vary with the time of day. A numerical solution is proposed; it is proved that the sequence of the numerical solution is contractive with a unique fixed point obtainable for any initial guess. We apply both to the departure time planning problem for a transportation operator that offers repetitive mandatory trips on a dense network. The case study offers two insights into departure time choice analyses. First, the assumption that the travel time variance at peak hours is constant induces biases for the optimal departure time. However, this assumption provides plausible results for the off-peak period. Second, travelers relying on personal judgment may have significantly different costs of travel than passengers making their decisions based on the system's recommendations. • A methodological framework for a departure time guidance system is proposed. • The method considers the time-varying nature of the dense urban networks. • A real departure time guidance is applied to commuting trips of service personnel with a fixed work start time. • Results reflect two caveats as far as departure time choice analyses are concerned. [ABSTRACT FROM AUTHOR]

Planning the public transportation system of a city is a complex process that depends on various factors, including transportation modes, origin–destination demands, service quality and reliability, and operational costs. The vehicle frequency setting (FS) problem is a particularly challenging aspect of this planning process. This work proposes a novel methodology, based on biased random-key genetic algorithms (BRKGA), for optimizing the FS of a bus-based public transport system. The proposed approach considers two optimization models that aim to address the following key metrics: (i) passengers' waiting time, and (ii) the operational cost for the concessionaire company, specifically the distance covered by buses. We apply our BRKGA methodology to a real case study using bus transport data from the city of Maceió (AL, Brazil). Our results demonstrate that, for each metric, the proposed methodology improves the performance of the city's public transport system by over 10%, compared to the current configuration. • A novel approach using BRKGA to optimize bus frequency in public transport systems. • Two models proposed: one minimizes waiting time, the other bus distance. • A comparison between the proposed solutions and the city's current system. • The method enhances the current city's transport configuration by over 10%. [ABSTRACT FROM AUTHOR]

Travel demands in remote areas are scattered and unstable. Thus, the integration of different types of transportation can be beneficial for improving the service level of transportation systems in remote areas. This study proposes a two-stage stochastic programming (SP) model for the integrated planning of a fixed-route–fixed-schedule bus service and dial-a-ride (DAR) transportation (DART) in remote areas. In the first stage, the route and timetable of a bus line are optimized as a set partitioning problem. In the second stage, DART is formulated as a pickup and delivery vehicle routing problem with time windows (PDVRPTW). A hybrid algorithm is developed to solve the two-stage SP problem. Case studies are presented to validate the planning model and the solution algorithm. Results show that the integration of bus and DAR services and the implementation of DART as PDVRPTW considerably reduce unserved demands and improve the service of the entire system. [ABSTRACT FROM AUTHOR]

This work focuses on freight consolidation in multimodal rail and road transportation. The transportation network comprises of a centralized source warehouse, a set of destination warehouses, and intermediate transshipment terminals. The paper formulates a single period integer programming model and a multi-period constraint programming model for optimizing multimodal freight transportation with volume discount on rail freight rate. The first model is aimed at consignee organizations with comparatively low shipment quantity and less frequent transportation, while the second model targets organizations which require continuous freight transportation on a massive scale. The single-period model is solved using a commercially available CPLEX solver. However, due to the computational complexity of the multi-period constraint programming model, a heuristic named Rail and Truck Allocation Algorithm (RATAA) is proposed. Computational experiments are carried out to prove the efficiency of the proposed solution approaches. [ABSTRACT FROM AUTHOR]

The article focuses on Placing Engagement at the Heart of Equitable Transportation Planning.

Over the past several decades, academics, practitioners, and policymakers have largely favored using the concept of transportation accessibility. The most common basis of the concept is Hansen's 1959 definition, the "potential of opportunities for interaction." Since then, most studies have comprehensively used this accessibility concept, along with other similar notions. This study began suspecting that, despite the concept's predominance in transportation planning, its evaluation and application have been conducted erroneously. In their regression-type analyses, many researchers have used accessibility as an independent variable based on the assumption that accessibility is controllable. The assumption originates from studies that accessibility shapes land use. As their theoretical ground, Hansen's study has been often referred to and by revisiting the study, we attempted to differently interpret his accessibility equation and to extend its application, and accordingly, we identified three possibilities. The first possibility is that land use can shape accessibility, not only that accessibility shapes land use. Second, while most previous studies used accessibility as an independent variable, such an application may not be generalizable. Last, according to the former two possibilities, the land use–transportation accessibility relationship may be reciprocal, so the potential for the endogeneity bias—specifically, simultaneity—needs to be considered. [ABSTRACT FROM AUTHOR]

The article discusses that the Rural Transportation Improvement Program 2023-2026 for the San Antonio District of the Texas Department of Transportation outlines regionally significant projects, including federally-funded ones, for the next 4 years.

The article offers information on the British Columbia (BC) Interior Section of the Institute of Transportation Engineers (ITE) which supports transportation planning professionals is by providing networking opportunities and professional development events.

• A quantitative approach to examine policy preference conflicts among diverse stakeholders across multiple plans. • Hazard mitigation plan incorporated the most overall stakeholder policy preferences among the four examined plans. • The regional transportation plan incorporated the fewest overall stakeholder policy preferences. • The hazard mitigation plan and the regional conservation plan had the highest level of policy consistency. • The hazard mitigation plan and regional transportation plan had the lowest level of policy consistency. The objective of this paper was to create and test a methodological framework for examining the extent to which various plans captured diverse stakeholder policy preferences related to resilience planning and management of interdependent urban systems. Policy preferences represent what were important for stakeholders and determine the priorities of stakeholders in resilience planning of urban systems. Stakeholders in different urban sectors may have conflicts of policy preferences in the resilience planning process. A comprehensive understanding of the extent to which plans incorporated and reflected policy preferences of different stakeholders would greatly improve the quality of resilience planning. Hence, we proposed a plan evaluation framework to examine the extent to which various plans captured diverse stakeholder policy preferences in resilience planning of interdependent infrastructure systems. We showed the application of the proposed framework in the evaluation of four plans affecting flood resilience planning in the Houston area. The proposed tool could help identify conflicted stakeholder policy preferences in planning and enable evaluation of policy consistency in networks of plans. [ABSTRACT FROM AUTHOR]

The article focuses on Trip Generation Manual methodologies estimate future traffic volumes, not accurate calculations, two foundational assumptions exist and one is that trip generation is exclusively dependent on one independent variable.

There is a relationship between choosing an activity and its duration, especially for non-mandatory activities. A number of studies have analyzed the decisions about an activity type and its duration independently, while some others have used joint models. This paper contributes to the body of knowledge using nested-logit and copula-based models for assessing the existence of interdependency between, or a hierarchy of, the choice of non-mandatory activity and its relative duration. In the case of the nestedlogit model, it is assumed that error terms of these decisions are interrelated, although one is influenced by another. In contrast, the copula-based model can facilitate making a spatial error correlation between observational units without imposing the assumption of restrictive distribution on the dependency structures between the error components. The data available from Qazvin, a city in Iran, were used for estimating both nested-logit and copula-based models and the best variables explaining both choices for each model were selected. The final models were compared in terms of log-likelihood at convergence and adjusted likelihood ratio index. The results indicated that there were some common influential observed and unobserved factors between these decisions. Also, the copula-based joint model with 20 equal to 0.134 outperformed nested-logit models and provided better explanatory power. [ABSTRACT FROM AUTHOR]

Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rapid urbanization and accelerated economic development are witnessed in Indian cities, causing city dwellers' auto- dependency on private modes of transport and has raised questions over the sustainability of transport infrastructure in cities. Traditionally, India's urban transport infrastructure was developed with the concept of "moving vehicles rather than people." Thus, the public transport system is inadequate and underdeveloped in most of the Indian cities. However, several initiatives were taken by the Indian government to promote sustainability and transit services were introduced into cities. The newly introduced formal modes of public transport lack integration with the previously existing transport mode at the operational, institutional, and informational level. Also, the physical and fare integration between the modes are missing in most of the Indian cities. With this motivation, the present research tries to understand the evolution of multimodal transport planning in Indian cities. For the present study, 49 planning documents were collected. An attempt was made to analyse the metadata and content of the planning documents dedicated to "Multimodal transport planning." The main aim was to assess how multimodal transport has been considered while planning the urban transport infrastructure. The challenges for integrated multimodal transport systems were identified, and appropriate conclusions were derived based on the study. [ABSTRACT FROM AUTHOR]