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Self-assembly is the most promising low-cost and high-throughput methodology for nanofabrication. This paper reports the optimization of a self-assembly process at room temperature for the growth of copper oxide (CuO) based nanostructures over a copper substrate using aqueous potassium hydroxide (KOH) solution as the oxidizing agent. The monoclinic phase of CuO nanostructures grown over the copper substrate was confirmed from the X-ray diffraction (XRD) and micro-Raman analysis. The overall chemical composition of nanostructures was confirmed to be that of CuO from its oxidation state using X-ray photoelectron spectroscopy (XPS). Photodetectors were engineered with the structure Cu/CuO/Ag. The photodetectors exhibited a response to both ultraviolet and visible light illumination. The optimized Cu/CuO/Ag structure exhibits a responsivity of ~ 1.65 µA/W, with an ON:OFF ratio of ~ 69 under a bias voltage of 0.01 V. The temporal dependence of photo-response for the optimized photodetector displayed the persistent nature of photoconduction indicating a delay in charge carrier recombination which could potentially be exploited for photovoltaic applications.

This study aims to present a method to analyze the problems that arise from the increasing use of shared mobility systems to solve the current problems of congestion. The use of shared and/or autonomous vehicles (SAVs) is beneficial since it could reduce the number of vehicles on the roads. On the other hand, the increased usage of SAVs to fulfill the same transportation needs would affect the total life of those vehicles. Current cost models are based on average values and life cycles of 15 years; they may not be useful in the case of increased daily travel miles of vehicles when subscription-based shared mobility systems become prevalent. In this paper, we present a comprehensive framework which considers the interactions of new cost structures and the system performance under new mobility schemes. This paper envisions the scenarios that will exemplify the importance of considering the changing costs because of the increased mileage of vehicles. Cost functions are properly designed to assess the new scenarios, a pricing scheme is introduced from the service provider’s point of view to adjust the demand for the services and the profits, an agent-based simulation platform is presented, and a framework is demonstrated to consider the interactions and optimize the system.

Remarkable feats of the past often leave behind historical pointers leaving us mesmerised. One such episode is that of the establishment of a magnetic and meteorological observatory on the peak of the Agasthiyar mountain 6200 feet above the sea level on the western Ghats in the year 1855 by John Allan Broun under the princely state of Travancore. It had facilities far ahead of its times representing the commitment to scientific universalism by its patrons. Broun established the observatory on the Agasthiyar mountain peak and undertook a meticulous study overcoming daunting geographical constrains. Broun was awarded the Keith Medal from the Royal Society of Edinburgh for the period 1859–1861. He received the Royal Medal from the Royal Society of London in 1878. He passed away in 1879. Remnants of the magnetic observatory which earned Broun scientific acclaim remain undiscovered, clouded within the Agasthiyar mountains.

Magnesium fuel cells deliver higher electrical power output than lithium-ion batteries and have the potential to become an economically attractive alternative power source for domestic purposes. In place of noble metals in the air cathode of Mg fuel cells, we investigate the use of an integrated structure of a catalyst and current collector composed of a Cu/CuO hetero-structure. For a single cell, comprising of electrodes of dimension 3 cm × 1.5 cm with aqueous NaCl as the electrolyte, the Mg- Cu/CuO-based fuel cell shows an open-circuit voltage of 0.7 V and discharge current drain rate of 0.45 mA/s. A power density of 8.75 µWcm−2is obtained with a CuO-based cathode when 1 M NaCl electrolyte is used. Relative to the magnesium-carbon-based fuel cell, the Mg-Cu/CuO fuel cell shows improved stability of the anode and cathode materials and extended operational time.

Abstract: A multi-faceted energy intensive technology that can be used for water disinfection and synthesis of electrolysed water (EW) is the need of the hour to achieve a sustainable post COVID 19 water management strategy. Direct sunlight driven processes are legislatively green technologies and hold the key in environmental sustenance. The development of a laboratory proto type reactor powered by a photovoltaic module for the treatment open source river water is described in this paper. This paper reports on the efficacy of the developed proto type technology for multipurpose application namely: (1) the production of Electrolysed water (EW) in a cost efficient manner using direct sunlight and (2) the removal of organic impurity from water using direct sunlight without the use of any photo catalyst or membrane. The prototype reactor utilizes chemical spray pyrolysis deposited highly photo-conducting indium sulphide thin films grown on fluorine doped tin oxide (F:SnO2) substrate (coated using chemical spray pyrolysis technique in-house) as the photo electrode. Dissolved organic matter arising in river water has distinctive fluorescence properties, and this research has utilized it to identify dissolved organic substances in both random samples and treated water. The work proves that photovoltaic module powered electrolytic reactors consisting of In2S3electrodes can be used for treatment of river water. A diaphragm free, energy intensive route for the production of electrolysed water with the use of non-hazardous NaCl as the electrolyte has been demonstrated here. We conclude that In2S3electrodes can be used for non-photo catalytic reduction of humic-derived impurities in river water. These results are also encouraging on the prospects of treating Nitrates present in the river water. The likes of techniques as described in this report that do not use photo catalyst or membranes may pave way for real time photovoltaic module powered floating reactors that can decontaminate water bodies on a large scale. The technique used by us demonstrates that a chlorine free route can be optimized for the synthesis of EW eliminating the production of large amounts of wastewater with high levels of biological oxygen demand (BOD).

Carbon quantum dots (CQDs) with an approximate size of 8 nm were prepared by a simple and cost-effective green technique—pyrolysis of cotton. Synthesized CQDs exhibit broad fluorescence covering almost blue and green light wavelengths. They show low cytotoxicity and good photostability. A simple mixing of rhodamine 6G chloride with highly fluorescent carbon dots in water led to aggregation of dye molecules on carbon dot surface. Controlling the emission of free rhodamine 6G dye with that of the resultant carbon dot-aggregated rhodamine 6G composite resulted in efficient white light emission with the CIE coordinate (0.305, 0.313). Suitable mixture of blue-light-emitting CQD in aqueous solution and rhodamine 6G dye can potentially be applied for tunable white light emission.

Peer-to-peer (P2P) ridesharing is a relatively new concept that aims to provide a sustainable method for transportation in urban areas. Previous studies have demonstrated that a system that incorporates both P2P ridesharing and transit would enhance mobility. We develop schemes to provide travel alternatives, routes and information across multiple modes, which includes P2P ridesharing, transit, city bike-sharing and walking, within the network. This study includes a case study of the operation of the multimodal system that includes P2P ridesharing participants (both drivers and riders), the Los Angeles Metro Red line subway rail, and the Los Angeles downtown bike-share system. The study conducts a simulation, enhanced by an optimization layer, of providing travel alternatives to passengers during morning peak hours. The results indicate that a multi-modal network expands the coverage of public transit, and that ride- and bike-sharing could be effective transit feeders when properly designed and integrated into the transit system.

Successful post-disaster response and recovery depends on prompt restoration of infrastructure, including transportation or communications. However, disasters can have an impact on the workforce responsible for restoration, for example, by damaging their homes. This study has two goals: 1. Identify workers potentially participating in restoring transportation and communications infrastructure; 2. Calculate these workers’ exposure to the peak ground accelerations (PGAs) of a 7.8 magnitude earthquake in a Southern California scenario, and compare it with the rest of the working population’s exposure. Four steps are required. First, calculate the mean PGA for each affected public use microdata area (PUMA). Second, identify the infrastructure restoration workforce by specifying Standard Occupational Classification (SOC) and North American Industry Classification System (NAICS) codes. When specifying, use the Emergency Support Function (ESF) Annexes for Transportation (ESF#1) and Communications (ESF#2) to clarify workers’ roles and responsibilities. This ESF-specific listing of codes is a novel contribution. Third, via frequency table, calculate the mean and standard deviation of transportation and communications workers’ exposure to PGAs in their PUMAs of residence. Finally, test the difference in mean PGA exposures between two populations: (a) transportation or communications workers and (b) the rest of the working population. This study finds that, for this scenario, transportation workers are exposed to statistically significant higher PGAs than non-transportation workers, and communication workers to significantly lower PGAs. For practitioners, knowing which worker categories a disaster disproportionately affects could justify pre-event investments in workforce preparedness and recovery planning efforts.

Background: The heat transfer condition at the interface of two fluids is an important factor that affects the stability characteristics of a half floating zone with temperature driven Marangoni convection. Various relevant papers and patents report that under microgravity conditions the critical temperature difference beyond which the onset of oscillatory behavior occurs gets drastically affected by the volume ratio of the half-floating zone. Hence, the actual mechanism and influence of parameters that affect flow structure for different volume ratios is still an area of research interest. Objective: To investigate the effect of viscous stresses exerted by the counter-directed ambient air flow on flow and thermal characteristics of a liquid bridge of a high Pr fluid with convex interface under microgravity conditions. Methods: In the present study, thermo-capillary convection in half floating zone is simulated using an axisymmetric model. Computations are carried out using commercial software ANSYS Fluent 17.2 with dimensional variables in both liquid and air domains. As thermo-capillary flow is laminar and incompressible in nature, pressure based solver with SIMPLE algorithm has been used in present analysis. Results: The study indicates that ambient air velocity has a significant influence on the variation of local surface velocity, local surface temperature and local Biot number at the convex interface. Flow and thermal fields inside liquid bridges are presented using isolines of stream function and temperature. Conclusion: With the increase in ambient air velocity, the recirculating regions formed around the halffloating- zone are found to shrink in their size affecting the heat transfer conditions at the interface.

Challenges arise in dynamic traffic assignment (DTA) when heterogeneous users evaluate choices on the basis of multiple interrelated criteria such as travel time and travel time uncertainty. This paper proposes a density-based formulation along with a stochastic quasi-gradient projection (SQGP) solution scheme with the aid of a traffic simulator. Path-dependent link cost is proposed to allow for the objective function formulation and more tractable analysis. The criteria in the discussion and the case study are travel time (link-additive), monetary cost (nonadditive), and travel time uncertainty (path-dependent link-additive). An information entropy-based uncertainty measure is proposed because of concerns about using conventional measures such as variability and reliability. The case study shows stochastic and efficient convergence, demonstrates the ability of SQGP to bypass local optima, and exemplifies the significant effect of using path-independent and path-dependent link costs to forecast traffic pattern and toll revenue. The results also suggest that a pricing strategy aimed at optimizing travel time and reliability for different user classes should consider travel time correlations between toll segments and the adjacent no-toll segments if enumerating paths is practically infeasible.

The energy communities have the potential to accelerate energy transition and empower consumers, thereby, promoting collaborative social transformation. The local energy communities can support the power grid by offering a variety of flexibility services through demand response, load shifting and energy storage. However, existing electricity markets, tariffs and regulations often hindering effective and sustainable solutions. This paper provides a comprehensive review about designing flexibility provisions for local energy communities in the context of existing and emerging flexible electricity markets. Further, it also discusses the need for social arrangements, technical designs, and their impact on energy communities. Based on reviewed literature findings and from the research projects at Department of Energy, Aalborg University as a part of the SERENE and SUSTENANCE EU Horizon 2020 projects involving sustainable energy communities, future directions will be highlighted.

Peer-to-peer (P2P) ridesharing is a recently emerging travel alternative that can help accommodate the growth in urban travel demand and at the same time alleviate problems such as excessive vehicular emissions. Prior ridesharing projects suggest that the demand for ridesharing is usually shifted from transit, but its true benefits are realized when the demand shifts from single-occupancy vehicles. This study investigated the potential of shifting demand from private autos to transit by providing a general modeling framework that found routes for private vehicle users that were a combination of P2P ridesharing and transit. The Los Angeles Metro Red Line in California was considered for a case study because it has recently shown declining ridership trends. For successful implementation of a ridesharing system, strategically selecting locations for individuals to get on and off the rideshare vehicles is crucial, along with an appropriate pricing structure for the rides. The study conducted a parametric analysis of the application of real-time P2P ridesharing to feed the Los Angeles Metro Red Line with simulated demand. A mobile application with an innovative ride-matching algorithm was developed as a decision support tool that suggested transit-rideshare and rideshare routes.

This paper proposes and illustrates a methodology to predict the fraction of time motor vehicles spend in different operating conditions from readily observable variables called emission specific characteristics (ESC). ESC describe salient characteristics of vehicles, roadway geometry, the roadside environment, traffic, and driving style (aggressive, normal, and calm). The information generated by our methodology can then be entered in vehicular emission models that rely on vehicle specific power, i.e., comprehensive modal emissions model (CMEM), international vehicle emissions (IVE), or motor vehicle emission simulator (MOVES) to compute energy consumption and vehicular emissions for various air pollutants. After generating second-by-second vehicle trajectories from a calibrated micro-simulation model, the authors estimated structural equation models to examine the influence of link ESC on vehicle operation. Authors' results show that 67% of the link speed variance is explained by ESC. Overall, the roadway geometry exerts a greater influence on link speed than traffic characteristics, the roadside environment, and driving style. Moreover, the speed limit has the strongest influence on vehicle operation, followed by facility type and driving style. Better understanding the impact on vehicle operation of ESC could help metropolitan planning organizations (MPOs) and regional transportation authorities predict vehicle operations and reduce the environmental footprint of motor vehicles.

Consumption of supply in transportation systems has generally always followed a First-Come-First-Served (FCFS) rule. This article proposes new control policies based on the concept of envy-freeness which outperform FCFS in both efficiency and fairness. We call an allocation envy-free when no agent feels any other agent’s allocation to be better than their own, at the current price. Envy-free allocations are thus considered fair. Several new contributions are made: we first present a conceptual theoretical supply-demand framework which formally introduces the new supply paradigm. We propose and simulate a new problem, queue-jumping operations on highways, in which vehicles can skip positions in a queue and compensate the overtaken vehicles with a payment. We present a new concept, dynamic envy-freeness, and provide a new envy ranking criterion, Constant Elasticity of Substitution Envy Intensity (CESEI) that is applied to PEXIC, an exchange-based traffic signal control scheme.

Challenges arise in dynamic traffic assignment (DTA) when heterogeneous users evaluate choices on the basis of multiple interrelated criteria such as travel time and travel time uncertainty. This paper proposes a density-based formulation along with a stochastic quasigradient projection (SQGP) solution scheme with the aid of a traffic simulator. Path-dependent link cost is proposed to allow for the objective function formulation and more tractable analysis. The criteria in the discussion and the case study are travel time (link-additive), monetary cost (non additive), and travel time uncertainty (path-dependent link-additive). An information entropy-based uncertainty measure is proposed because of concerns about using conventional measures such as variability and reliability. The case study shows stochastic and efficient convergence, demonstrates the ability of SQGP to bypass local optima, and exemplifies the significant effect of using path-independent and path-dependent link costs to forecast traffic pattern and toll revenue. The results also suggest that a pricing strategy aimed at optimizing travel time and reliability for different user classes should consider travel time correlations between toll segments and the adjacent no-toll segments if enumerating paths is practically infeasible.

Peer-to-peer (P2P) ridesharing is a recently emerging travel alternative that can help accommodate the growth in urban travel demand and at the same time alleviate problems such as excessive vehicular emissions. Prior ridesharing projects suggest that the demand for ridesharing is usually shifted from transit, but its true benefits are realized when the demand shifts from single-occupancy vehicles. This study investigated the potential of shifting demand from private autos to transit by providing a general modeling framework that found routes for private vehicle users that were a combination of P2P ridesharing and transit. The Los Angeles Metro Red Line in California was considered for a case study because it has recently shown declining ridership trends. For successful implementation of a ridesharing system, strategically selecting locations for individuals to get on and off the rideshare vehicles is crucial, along with an appropriate pricing structure for the rides. The study conducted a parametric analysis of the application of real-time P2P ridesharing to feed the Los Angeles Metro Red Line with simulated demand. A mobile application with an innovative ride-matching algorithm was developed as a decision support tool that suggested transit-rideshare and rideshare routes.

A simple eco-friendly method was employed to generate white light using pomegranate-PVA - curcumin mediated silver nanoparticles (CAg NP-PVA) mixture. The white light emission is obtained by integrating the green emitting curcumin and blue emitting pomegranate extract. Controlling the emission of curcumin with Ag NP-PVA and mixing with pomegranate extract resulted in efficient white light with the Commission Internationale d’Eclairage (CIE) chromaticity coordinate (0.28,0.31). Ag NP-PVA and anthocyanin played a significant role in obtaining CIE coordinate close to that of pure white light (0.33,0.33). The optimized white light emission obtained from pomegranate- CAg NP-PVA mixture is cheap and fairly green in nature.

Understanding the spatial variation of taxi ridership is of critical importance to many government agencies and taxi companies because taxis’ location dependency on spatial pattern of passenger demand results in spatially unbalanced taxi demand and supply. This study presents an analysis of the spatial distribution of taxi ridership by using large-scale GPS taxi trip data collected from Seoul, South Korea. To capture the spatial variations better in taxi ridership, GPS entities were disaggregated into units of a uniform size with a grid cell decomposition method. A geographically weighted spatial regression was applied to model spatial correlations of factors associated with transit and urban density to taxi ridership. Results from the proposed method demonstrated a higher relationship between taxi and subway ridership in the regions where lower accessibility to subway stations existed. In these regions, taxis were found to perform as a complementary mode to subway. In residential and commercial districts, this analysis showed that population and employment were highly related to taxi ridership. In contrast, in central business districts it was the building area (floor space), rather than population and employment, that was highly related to taxi ridership.

Understanding the spatial variation of taxi ridership is of critical importance to many government agencies and taxi companies because taxis’ location dependency on spatial pattern of passenger demand results in spatially unbalanced taxi demand and supply. This study presents an analysis of the spatial distribution of taxi ridership by using large-scale GPS taxi trip data collected from Seoul, South Korea. To capture the spatial variations better in taxi ridership, GPS entities were disaggregated into units of a uniform size with a grid cell decomposition method. A geographically weighted spatial regression was applied to model spatial correlations of factors associated with transit and urban density to taxi ridership. Results from the proposed method demonstrated a higher relationship between taxi and subway ridership in the regions where lower accessibility to subway stations existed. In these regions, taxis were found to perform as a complementary mode to subway. In residential and commercial districts, this analysis showed that population and employment were highly related to taxi ridership. In contrast, in central business districts it was the building area (floor space), rather than population and employment, that was highly related to taxi ridership.

Despite the prevalent use of simulation methods in a majority of areas in transportation research, transit and paratransit research has not relied heavily on simulation models, not only because traditional transit studies do not necessarily require simulation techniques but also because proper simulation packages are not available for modeling innovative transit systems. This study proposes a new type of simulation framework, targeting large-scale flexible transit systems with various vehicle operation schemes. The intent of this study is to describe the generalized concepts and the detailed architecture of the framework developed for urban transportation networks. A comprehensive process for the framework development, including considerations of simulation data conversion and user interface design, is discussed. Two real-time flexible transit applications are modeled with the proposed simulation framework: high coverage point-to-point transit and real-time shuttle service. Detailed simulation results are provided, and the importance of simulation design and operational features is addressed. The results also show that the framework can model realistic large networks several times faster than real time with standard desktop computers; these results raise the possibility of its use in real-time optimization schemes as well as for synchronous modeling with commercial road traffic simulators.

Along with the increasing popularity of mobile smart devices such as smartphones and tablet personal computers, traffic information is coming to drivers through many intelligent traffic applications. Many countries have multiple traffic information service providers (ISPs), who make every effort to improve their service quality to influence more people to subscribe to their services. Because such a commercial environment is developing, a study of the effect of multiple ISPs on road network performance is necessary. Therefore, a modeling framework of day-to-day dynamics in which multiple ISPs compete and cooperate with each other to enhance their subscriber service quality is developed in this study. A realistic information acquisition and learning mechanism that ensures consistent updating of individually perceived day-by-day travel times is incorporated for driver behavior in this framework. A bounded rational behavior model was adopted for route choice decisions. The framework is capable of investigating the effects of any potential competition or cooperation of multiple ISPs in the traffic information market in terms of their information-sharing strategies. Numerical experiments on a real network were conducted to analyze the impact of such interactions on the network performance. The results showed that a cooperative system was not necessarily the best for network performance and that there was an optimal level of market penetration of traffic information services in transportation networks beyond which the benefits no longer increased or even worsened.

Along with the increasing popularity of mobile smart devices such as smartphones and tablet personal computers, traffic information is coming to drivers through many intelligent traffic applications. Many countries have multiple traffic information service providers (ISPs), who make every effort to improve their service quality to influence more people to subscribe to their services. Because such a commercial environment is developing, a study of the effect of multiple ISPs on road network performance is necessary. Therefore, a modeling framework of day-to-day dynamics in which multiple ISPs compete and cooperate with each other to enhance their subscriber service quality is developed in this study. A realistic information acquisition and learning mechanism that ensures consistent updating of individually perceived day-by-day travel times is incorporated for driver behavior in this framework. A bounded rational behavior model was adopted for route choice decisions. The framework is capable of investigating the effects of any potential competition or cooperation of multiple ISPs in the traffic information market in terms of their information-sharing strategies. Numerical experiments on a real network were conducted to analyze the impact of such interactions on the network performance. The results showed that a cooperative system was not necessarily the best for network performance and that there was an optimal level of market penetration of traffic information services in transportation networks beyond which the benefits no longer increased or even worsened.

Electric vehicles (EVs) have substantial potential for fleet applications as energy-efficient vehicles with fewer carbon-based emissions. However, EV utilization brings other concerns related to limited vehicle ranges and battery-charging issues, and thus the recharging infrastructure needs to be carefully designed and financially supported. This study focuses on the use of an EV fleet for an innovative transportation alternative called high-coverage point-to-point transit (HCPPT), which involves a sufficient number of deployed small vehicles that respond to real-time desires for point-to-point travel of individual travelers. As large-scale fleet operation of EVs and the HCPPT system are both envisaged only as future transportation alternatives, the benefits of combining these options have to be modeled and studied. Possible infrastructure investment benefits could result for both systems when transfer-hub locations of HCPPT are used as electric-charging locations. This paper studied the performance results from the simulation of a real-world transfer-hub system and developed certain vehicle-routing schemes to handle the specifics of EV operations. Alternative ways to schedule the charging of EVs were modeled with different insertion heuristics within vehicle-routing optimization. The results showed that the battery-charging limitations of EVs do not significantly affect the efficiency of the HCPPT system and revealed the importance of details such as the number of required charging stations at the hub locations.

A gradient projection method is presented to solve the problem with dynamic traffic assignment. The gradient projection scheme is a feasible direction algorithm that operates directly in the space of path flows. The algorithm was applied to a simulation-based model for dynamic traffic assignment that used microscopic simulation for supply-side modeling. The formulation used link cost functions that were separate for moving and queuing vehicles on each link, so that the formulation could effectively capture the effects of traffic dynamic characteristics on each group, especially in arterial networks. The model and solution algorithm were applied to a small real-world network, and the performance was studied. The gradient projection algorithm avoided the problem of convergence to nonoptimal conditions shown by the prevalent method of successive averages and demonstrated significantly faster convergence.

A gradient projection method is presented to solve the problem with dynamic traffic assignment. The gradient projection scheme is a feasible direction algorithm that operates directly in the space of path flows. The algorithm was applied to a simulation-based model for dynamic traffic assignment that used microscopic simulation for supply-side modeling. The formulation used link cost functions that were separate for moving and queuing vehicles on each link, so that the formulation could effectively capture the effects of traffic dynamic characteristics on each group, especially in arterial networks. The model and solution algorithm were applied to a small real-world network, and the performance was studied. The gradient projection algorithm avoided the problem of convergence to nonoptimal conditions shown by the prevalent method of successive averages and demonstrated significantly faster convergence.

This study focuses on the optimization and simulation modeling associated with the design of alternative transportation, the high-coverage point-to-point transit (HCPPT), which involves a sufficient number of deployed small vehicles with advanced-information supply schemes. This paper identifies the inefficiency of the existing heuristic rules for vehicle routing and proposes a new optimization approach for an HCPPT solution. A path-based model for routing through multiple hubs as opposed to a single pair of hubs is formulated to improve HCPPT operational schemes. This study also develops a simulation framework for the application of the proposed algorithm. To illustrate the system and computational performances of the proposed model, simulations are conducted with different sets of scenarios and model parameters. The path-based model shows reasonable performance over the various demand patterns in level of service and ride time index. It is also shown that, with the use of constraint-driven schemes and model parameters, the scale of the problem is reduced. The computational times are shown to be quite small, and demonstrate the viability in real-time operations.

This study focuses on the optimization and simulation modeling associated with the design of alternative transportation, the high-coverage point-to-point transit (HCPPT), which involves a sufficient number of deployed small vehicles with advanced-information supply schemes. This paper identifies the inefficiency of the existing heuristic rules for vehicle routing and proposes a new optimization approach for an HCPPT solution. A path-based model for routing through multiple hubs as opposed to a single pair of hubs is formulated to improve HCPPT operational schemes. This study also develops a simulation framework for the application of the proposed algorithm. To illustrate the system and computational performances of the proposed model, simulations are conducted with different sets of scenarios and model parameters. The path-based model shows reasonable performance over the various demand patterns in level of service and ride time index. It is also shown that, with the use of constraint-driven schemes and model parameters, the scale of the problem is reduced. The computational times are shown to be quite small, and demonstrate the viability in real-time operations.

As dynamic traffic simulation (DTS) becomes ever more popular, how to provide the input data, that is, a dynamic origin-destination (O-D) table, for simulation becomes an important question. During recent years, many researchers have proposed estimation methods for a dynamic O-D table from an academic point of view, but transportation engineers appear to use the dynamic approach in practical applications rarely. In recent simulation projects, ad hoc schemes have been used by practitioners for developing dynamic O-D tables for subareas from larger, static planning O-D tables without much theoretical soundness. The authors developed a dynamic O-D estimation model based on a path-flow approach and iterative simulations, along with a scheme to find a justifiable seed O-D table, and applied it to a real-world context. A major arterial corridor in Suwon, South Korea, was selected, and the link turning counts were collected at intersections. A microscopic DTS model, PARAMICS, was used with the dynamic O-D table to simulate link turning counts. The test results show that the developed O-D estimation technique is efficient, with simulated link traffic counts and section travel times very close to the reality.

The San Pedro Bay Ports (SPBP) complex of Los Angeles and Long Beach in Southern California is one of the largest container port complexes in the world. This complex contributes significantly to both regional and national economies in California and the United States, respectively. However, the ongoing growth and economic benefits of the SPBP are threatened by negative externalities associated with port operations, particularly increasing congestion and air pollution. The objective of this paper is to explore a new approach to estimating vehicle emission impacts of freight corridor operations related to the port area, particularly those associated with heavy-duty diesel trucks. The paper combines a microscopic traffic simulation model to capture detailed vehicle trajectories (speeds and accelerations) and congestion effects, with an emission model and a spatial dispersion model to facilitate the estimation of the health and environmental justice impacts of freight corridor operations. Focus is on the I-710 freeway in the Alameda Corridor from the SPBP area up to downtown Los Angeles, California, some 20 mi north. Several scenarios were evaluated in addition to the 2005 base scenario: replacement of the current fleet of port heavy-duty diesel trucks with zero-emission vehicles, elimination of port heavy-duty diesel truck trips that would correspond to shifting more containers to other modes such as rail, and implementation of a truckrestricted lane, preventing trucks from using the leftmost lanes. The results show that fleet replacement with cleaner trucks yields the most emission reductions both quantitatively and spatially.

This study formulates a hierarchical Bayesian mixture model for estimating travel time distributions along freeway sections by using small data samples from vehicle probes, which have been collected over multiple days. Two normal components are used to capture the heterogeneity in the experienced travel times and to model various distributional shapes generally known to be skewed or multimodal. Travel time data collected during different intervals under similar traffic conditions are used to construct the prior for model parameters via a hierarchical Bayesian formulation. The posterior distributions can be continuously updated as new data from probes become available, and are used for prediction under different levels of data availability. A simulation study shows that true travel time distribution for each section during each interval can be well-approximated with the use of this proposed model.

This study formulates a hierarchical Bayesian mixture model for estimating travel time distributions along freeway sections by using small data samples from vehicle probes, which have been collected over multiple days. Two normal components are used to capture the heterogeneity in the experienced travel times and to model various distributional shapes generally known to be skewed or multimodal. Travel time data collected during different intervals under similar traffic conditions are used to construct the prior for model parameters via a hierarchical Bayesian formulation. The posterior distributions can be continuously updated as new data from probes become available, and are used for prediction under different levels of data availability. A simulation study shows that true travel time distribution for each section during each interval can be well-approximated with the use of this proposed model.

As dynamic traffic simulation (DTS) becomes ever more popular, how to provide the input data, that is, a dynamic origin–destination (O-D) table, for simulation becomes an important question. During recent years, many researchers have proposed estimation methods for a dynamic O-D table from an academic point of view, but transportation engineers appear to use the dynamic approach in practical applications rarely. In recent simulation projects, ad hoc schemes have been used by practitioners for developing dynamic O-D tables for subareas from larger, static planning O-D tables without much theoretical soundness. The authors developed a dynamic O-D estimation model based on a path-flow approach and iterative simulations, along with a scheme to find a justifiable seed O-D table, and applied it to a real-world context. A major arterial corridor in Suwon, South Korea, was selected, and the link turning counts were collected at intersections. A microscopic DTS model, PARAMICS, was used with the dynamic O-D table to simulate link turning counts. The test results show that the developed O-D estimation technique is efficient, with simulated link traffic counts and section travel times very close to the reality.

An activity chaining model described as a time use problem is presented. Unlike existing activity chaining models, the time use problem includes all choice attributes related to time, not only when and where the activity is performed but also the duration of the activity. To apply the time use problem to transportation network demand analysis, the opportunity cost of travel time is modeled through a constraint approach, and the value of activity is represented by time-dependent utility profiles. In the model, among activity alternatives, the activity with the biggest utility is sequentially included within an individual's daily disposable income. Through the sequential selection process, not only the activity type but also its start time and duration are simultaneously determined on the basis of an individual's perceived time-dependent travel time for the activity. Since the activity chaining is performed on the basis of the dynamic travel times calculated via traffic network simulation, the activity chaining is iteratively updated through a day-to-day dynamic process. The model is applied to a medium-size network to show its applicability. The example sheds light on the practicality of activity-based models and extends applications of the travel demand model to market analysis by illustrating how the model can analyze impact of transportation network improvement on the activity pattern and zonal socioeconomic conditions particularly associated with shopping and leisure activities.

An activity chaining model described as a time use problem is presented. Unlike existing activity chaining models, the time use problem includes all choice attributes related to time, not only when and where the activity is performed but also the duration of the activity. To apply the time use problem to transportation network demand analysis, the opportunity cost of travel time is modeled through a constraint approach, and the value of activity is represented by time-dependent utility profiles. In the model, among activity alternatives, the activity with the biggest utility is sequentially included within an individual's daily disposable income. Through the sequential selection process, not only the activity type but also its start time and duration are simultaneously determined on the basis of an individual's perceived time-dependent travel time for the activity. Since the activity chaining is performed on the basis of the dynamic travel times calculated via traffic network simulation, the activity chaining is iteratively updated through a day-to-day dynamic process. The model is applied to a medium-size network to show its applicability. The example sheds light on the practicality of activity-based models and extends applications of the travel demand model to market analysis by illustrating how the model can analyze impact of transportation network improvement on the activity pattern and zonal socioeconomic conditions particularly associated with shopping and leisure activities.

A novel distributed method for estimating a trip table in real time is described. The system is called "persistent traffic cookies" by analogy with the use of cookies by web servers to keep track of the current state of web browsers navigating a web site. The method uses traffic cookies placed on in-vehicle computers to maintain the state (current trip) of vehicles moving through the system. These cookies are persistent from day to day; taken together, they form a complete travel history for a traveler or vehicle. The method leverages the vehicles to store their own travel data and then physically do carry those data around the network. Advantages include scalability in both storage and computational effort as well as the unique ability to incorporate the travel behavior of individuals into real-time traffic predictions. A small-scale simulation is presented to illustrate the concept and its potential applications.

The aim of the real-time mass transport vehicle routing problem (MTVRP) is to find a solution to route n vehicles in real time to pick up and deliver m passengers. This problem is described in the context of flexible large-scale mass transportation options that use new technologies for communication among passengers and vehicles. This study does not focus on the technological aspects required to operate this kind of system but instead treats the theoretical approaches to provide a consistent mathematical formulation of the problem and its solution. This analytical effort is relevant to future transportation options involving large-scale real-time routing of shared-ride fleet transit vehicles. However, the global optimization of a complex system involving routing and scheduling multiple vehicles and passengers as well as design issues has not been specifically studied in the past. This research proposes a methodology to solve it by means of a three-level hierarchical optimization approach. Within the optimization process, a mass transport network design problem (MTNDP) is solved. The MTVRP is introduced and a scheme to solve it is presented. Then the associated algorithm to perform the MTNDP optimization is described in detail. A real-world application is solved for the MTNDP, showing promising results with regard to the applicability of the methodology for large-scale transit problems.

A novel distributed method for estimating a trip table in real time is described. The system is called “persistent traffic cookies” by analogy with the use of cookies by web servers to keep track of the current state of web browsers navigating a web site. The method uses traffic cookies placed on in-vehicle computers to maintain the state (current trip) of vehicles moving through the system. These cookies are persistent from day to day; taken together, they form a complete travel history for a traveler or vehicle. The method leverages the vehicles to store their own travel data and then physically do carry those data around the network. Advantages include scalability in both storage and computational effort as well as the unique ability to incorporate the travel behavior of individuals into real-time traffic predictions. A small-scale simulation is presented to illustrate the concept and its potential applications.

The aim of the real-time mass transport vehicle routing problem (MTVRP) is to find a solution to route nvehicles in real time to pick up and deliver mpassengers. This problem is described in the context of flexible large-scale mass transportation options that use new technologies for communication among passengers and vehicles. This study does not focus on the technological aspects required to operate this kind of system but instead treats the theoretical approaches to provide a consistent mathematical formulation of the problem and its solution. This analytical effort is relevant to future transportation options involving large-scale real-time routing of shared-ride fleet transit vehicles. However, the global optimization of a complex system involving routing and scheduling multiple vehicles and passengers as well as design issues has not been specifically studied in the past. This research proposes a methodology to solve it by means of a three-level hierarchical optimization approach. Within the optimization process, a mass transport network design problem (MTNDP) is solved. The MTVRP is introduced and a scheme to solve it is presented. Then the associated algorithm to perform the MTNDP optimization is described in detail. A real-world application is solved for the MTNDP, showing promising results with regard to the applicability of the methodology for large-scale transit problems.

The use of microsimulation is not prevalent in modeling transit systems. The software available is rarely capable of modeling transit systems that route vehicles in real time or systems that adjust to the level of demand. It is not straightforward to model a transit system with the available simulation packages, especially when the transit scheme is not “standard.” Even the simulation of basic schemes such as a streetcar system or a paratransit service is not an option in existing simulation software. A cursory study would reveal that simulation of any vehicle class other than personal automobiles is always developed as an afterthought in the existing large-scale microsimulation packages. In most cases, simulation developers have done only a superficial addition of transit simulation on top of detailed simulation of automobiles and control mechanisms on freeways and arterials. A microsimulation framework has been developed for a general transit system. Concrete applications of such a framework have been developed and are presented in this paper. Two different transit systems are modeled using the same general framework to show the flexibility of the simulation scheme: a bus rapid transit system and a large-scale real-time routed transit design. The intent is to describe the flexibility that can be brought into a properly designed microscopic simulation platform to model innovative transit system designs, especially in larger urban networks. A simulation scenario is presented in which both transit systems function. The details of both simulations are described along with the problems encountered, and ways to solve them are exposed.

The use of micros!miilalion is not prevalent in modeling transit systems. The software available is rarely capable of modeling transit systems that route vehicles in real time or systems that adjust to the level of demand. It is not straightforward to model a transit system with the available simulation packages, especially when the transit scheme is not "standard." Even the simulation of basic schemes such as a streetcar system or a paratransit service is not an option in existing simulation software. A cursory study would reveal that simulation of any vehicle class other than personal automobiles is always developed as an afterthought in the existing large-scale microsimulation packages. In most cases, simulation developers have done only a superficial addition of transit simulation on top of detailed simulation of automobiles and control mechanisms on freeways and arterials. A microsimulation framework has been developed for a general transit system. Concrete applications of such a framework have been developed and are presented in this paper. Two different transit systems are modeled using the same general framework to show the flexibility of the simulation scheme: a bus rapid transit system and a large-scale real-time routed transit design. The intent is to describe the flexibility that can be brought into a properly designed microscopic simulation platform to model innovative transit system designs, especially in larger urban networks. A simulation scenario is presented in which both transit systems function. The details of both simulations are described along with the problems encountered, and ways to solve them are exposed.

Computational efficiency and first in, first out (FIFO) are both important issues in applications such as dynamic traffic assignment. This paper first shows by example the FIFO properties of a computationally efficient commodity-based kinematic wave (CKW) model of network traffic flow. After the development of measurements of FIFO violation among commodities in location and time on the basis of the misplacement of vehicles in total and commodity traffic, it is argued theoretically that numerical CKW solutions converge to FIFO ones. Then, with numerical examples, it is shown that FIFO violation in CKW solutions is a function of the simulation time and the number of commodities but decreases with decreasing cell lengths, even for infinite number of commodities. Finally, the possible implications of this study are discussed.

In many major metropolitan areas, taxi services have played an important role as a semipublic transportation mode without public support. However, there has not been much modeling effort-despite the importance of taxis in urban transportation systems-mainly because of the difficulty in modeling taxi drivers' behavior. This study models a taxi service system in urban areas, taking into account taxi drivers' knowledge of the transportation network built from their day-to-day experience. Passenger-seeking behavior by taxi drivers is modeled on the basis of their expected travel time and expected waiting time. The model considers the stochastic and dynamic transportation network and various levels of network knowledge on the part of drivers. This modeling approach provides flexibility in modeling the characteristics of taxi operation as well as understanding how taxi drivers' capability evolves. The study analyzes the fleet size of taxi service systems and the effects of the taxi company's information systems by considering quality and operational efficiency of taxi services, from both the passengers' and taxi operators' points of view. A simulation experiment shows that the taxi information system can provide benefits equivalent to increasing the number of taxis by 20% in regard to the quality of taxi service.

Computational efficiency and first in, first out (FIFO) are both important issues in applications such as dynamic traffic assignment. This paper first shows by example the FIFO properties of a computationally efficient commodity-based kinematic wave (CKW) model of network traffic flow. After the development of measurements of FIFO violation among commodities in location and time on the basis of the misplacement of vehicles in total and commodity traffic, it is argued theoretically that numerical CKW solutions converge to FIFO ones. Then, with numerical examples, it is shown that FIFO violation in CKW solutions is a function of the simulation time and the number of commodities but decreases with decreasing cell lengths, even for infinite number of commodities. Finally, the possible implications of this study are discussed.

A complete optimal formulation of a network traffic control scheme in the form of arc flows in a time-expanded network is presented. The platoon dispersion model used in the proposed optimal control formulation forms linear constraints. The integer-linear network programming formulation is solved by using a modified network simplex and branch-and-bound scheme. The formulation does not assume fixed cycle lengths or phase sequences. It assumes full information on external inputs, but it can be incorporated into a sensor-based environment, as well as into a feedback control framework. The integer-linear program formulation may not be efficiently solved by standard simplex and branch-and-bound techniques. Network programming formulations that can be used to handle the linear equations and the integer constraints at the intersections are discussed. A special-purpose network simplex algorithm for fast solution is also mentioned. The optimization model takes the form of mixed integer-linear programming. The control strategies generated by these optimization models were compared with those derived from conventional signal timing models by using the TRAF-NETSIM microscopic simulation model. The proposed optimization models consistently outperformed the conventional signal control methods with respect to the system delay objective. It was found that the optimization models successfully produced optimal signal timing plans for the various signalized intersections, including simulated and real-world networks.