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This study investigates the implementation of semi-on-demand (SoD) hybrid-route services using Shared Autonomous Vehicles (SAVs) on existing transit lines. SoD services combine the cost efficiency of fixed-route buses with the flexibility of on-demand services. SAVs first serve all scheduled fixed-route stops, then drop off and pick up passengers in the pre-determined flexible-route portion, and return to the fixed route. This study addresses four key questions: optimal fleet and vehicle sizes for peak-hour fixed-route services with SAVs and during transition (from drivers to autonomous vehicles), optimal off-peak SoD service planning, and suitable use cases. The methodology combines analytical modeling for service planning with agent-based simulation for operational analysis. We examine ten bus routes in Munich, Germany, considering full SAV and transition scenarios with varying proportions of drivers. Our findings demonstrate that the lower operating costs of SAVs improve service quality through increased frequency and smaller vehicles, even in transition scenarios. The reduced headway lowers waiting time and also favors more flexible-route operation in SoD services. The optimal SoD settings range from fully flexible to hybrid routes, where higher occupancy from the terminus favors shorter flexible routes. During the transition phase, limited fleet size and higher headways constrain the benefits of flexible-route operations. The simulation results corroborate the SoD benefits of door-to-door convenience, attracting more passengers without excessive detours and operator costs at moderate flexible-route lengths, and validate the analytical model., Comment: 25 pages, 10 figures

In recent years, Paris, France, transformed its transportation infrastructure, marked by a notable reallocation of space away from cars to active modes of transportation. Key initiatives driving this transformation included Plan V\'elo I and II, during which the city created over 1,000 kilometres of new bike paths to encourage cycling. For this, substantial road capacity has been removed from the system. This transformation provides a unique opportunity to investigate the impact of the large-scale network re-configuration on the network-wide traffic flow. Using the Network Fundamental Diagram (NFD) and a re-sampling methodology for its estimation, we investigate with empirical loop detector data from 2010 and 2023 the impact on the network's capacity, critical density, and free-flow speed resulting from these policy interventions. We find that in the urban core with the most policy interventions, per lane capacity decreased by over 50%, accompanied by a 60% drop in free-flow speed. Similarly, in the zone with fewer interventions, capacity declined by 34%, with a 40% reduction in free-flow speed. While these changes seem substantial, the NFDs show that overall congestion did not increase, indicating a modal shift to other modes of transport and hence presumably more sustainable urban mobility.

Rapid urbanization and growing urban populations worldwide present significant challenges for cities, including increased traffic congestion and air pollution. Effective strategies are needed to manage traffic volumes and reduce emissions. In practice, traditional traffic flow simulations are used to test those strategies. However, high computational intensity usually limits their applicability in investigating a magnitude of different scenarios to evaluate best policies. This paper introduces an innovative approach to assess the effects of traffic policies using Graph Neural Networks (GNN). By incorporating complex transport network structures directly into the neural network, this approach could enable rapid testing of various policies without the delays associated with traditional simulations. We provide a proof of concept that GNNs can learn and predict changes in car volume resulting from capacity reduction policies. We train a GNN model based on a training set generated with a MATSim simulation for Paris, France. We analyze the model's performance across different road types and scenarios, finding that the GNN is generally able to learn the effects on edge-based traffic volume induced by policies. The model is especially successful in predicting changes on major streets. Nevertheless, the evaluation also showed that the current model has problems in predicting impacts of spatially small policies and changes in traffic volume in regions where no policy is applied due to spillovers and/or relocation of traffic.

Deep reinforcement learning has shown promise in various engineering applications, including vehicular traffic control. The non-stationary nature of traffic, especially in the lane-free environment with more degrees of freedom in vehicle behaviors, poses challenges for decision-making since a wrong action might lead to a catastrophic failure. In this paper, we propose a novel driving strategy for Connected and Automated Vehicles (CAVs) based on a competitive Multi-Agent Deep Deterministic Policy Gradient approach. The developed multi-agent deep reinforcement learning algorithm creates a dynamic and non-stationary scenario, mirroring real-world traffic complexities and making trained agents more robust. The algorithm's reward function is strategically and uniquely formulated to cover multiple vehicle control tasks, including maintaining desired speeds, overtaking, collision avoidance, and merging and diverging maneuvers. Moreover, additional considerations for both lateral and longitudinal passenger comfort and safety criteria are taken into account. We employed inter-vehicle forces, known as nudging and repulsive forces, to manage the maneuvers of CAVs in a lane-free traffic environment. The proposed driving algorithm is trained and evaluated on lane-free roads using the Simulation of Urban Mobility platform. Experimental results demonstrate the algorithm's efficacy in handling different objectives, highlighting its potential to enhance safety and efficiency in autonomous driving within lane-free traffic environments.

This study examines the route design of a semi-on-demand hybrid route directional service in the public transit network, offering on-demand flexible route service in low-density areas and fixed route service in higher-density areas with Shared Autonomous Mobility Service (SAMS). The study develops analytically tractable cost expressions that capture access, waiting, and riding costs for users, and distance-based operating and time-based vehicle costs for operators. Two formulations are presented for strategic and tactical decisions in flexible route portion, fleet size, headway, and vehicle size optimization, enabling the determination of route types between fixed, hybrid, and flexible routes based on demand, cost, and operational parameters. The practical applications and benefits of semi-on-demand feeders are demonstrated with numerical examples and a large-scale case study in the Chicago metropolitan area. Findings reveal scenarios in which flexible route portions serving passengers located further away reduce total costs, particularly user costs. Lower operating costs in lower-demand areas favor more flexible routes, whereas higher demand densities favor more traditional line-based operations. On two studied lines, a current cost forecast favors smaller vehicles with flexible routes, but operating constraints and higher operating costs would favor bigger vehicles with hybrid routes. The study provides an analytical tool to design SAMS as directional services and transit feeders, and tractable continuous approximation formulations for future research in transit network design., Comment: 24 pages, 12 figures, previous version presented at the 103rd Transportation Research Board Annual Meeting, Washington, D.C

Floating Car Observers (FCOs) are an innovative method to collect traffic data by deploying sensor-equipped vehicles to detect and locate other vehicles. We demonstrate that even a small penetration rate of FCOs can identify a significant amount of vehicles at a given intersection. This is achieved through the emulation of detection within a microscopic traffic simulation. Additionally, leveraging data from previous moments can enhance the detection of vehicles in the current frame. Our findings indicate that, with a 20-second observation window, it is possible to recover up to 20\% of vehicles that are not visible by FCOs in the current timestep. To exploit this, we developed a data-driven strategy, utilizing sequences of Bird's Eye View (BEV) representations of detected vehicles and deep learning models. This approach aims to bring currently undetected vehicles into view in the present moment, enhancing the currently detected vehicles. Results of different spatiotemporal architectures show that up to 41\% of the vehicles can be recovered into the current timestep at their current position. This enhancement enriches the information initially available by the FCO, allowing an improved estimation of traffic states and metrics (e.g. density and queue length) for improved implementation of traffic management strategies.

Accurate and comprehensive semantic segmentation of Bird's Eye View (BEV) is essential for ensuring safe and proactive navigation in autonomous driving. Although cooperative perception has exceeded the detection capabilities of single-agent systems, prevalent camera-based algorithms in cooperative perception neglect valuable information derived from historical observations. This limitation becomes critical during sensor failures or communication issues as cooperative perception reverts to single-agent perception, leading to degraded performance and incomplete BEV segmentation maps. This paper introduces TempCoBEV, a temporal module designed to incorporate historical cues into current observations, thereby improving the quality and reliability of BEV map segmentations. We propose an importance-guided attention architecture to effectively integrate temporal information that prioritizes relevant properties for BEV map segmentation. TempCoBEV is an independent temporal module that seamlessly integrates into state-of-the-art camera-based cooperative perception models. We demonstrate through extensive experiments on the OPV2V dataset that TempCoBEV performs better than non-temporal models in predicting current and future BEV map segmentations, particularly in scenarios involving communication failures. We show the efficacy of TempCoBEV and its capability to integrate historical cues into the current BEV map, improving predictions under optimal communication conditions by up to 2% and under communication failures by up to 19%. The code will be published on GitHub.

With the widespread adoption of mobility-on-demand (MoD) services and the advancements in autonomous vehicle (AV) technology, the research interest into the AVs based MoD (AMoD) services has grown immensely. Often agent-based simulation frameworks are used to study the AMoD services using the trip data of current Taxi or MoD services. For reliable results of AMoD simulations, a realistic city network and travel times play a crucial part. However, many times the researchers do not have access to the actual network state corresponding to the trip data used for AMoD simulations reducing the reliability of results. Therefore, this paper introduces a spatio-temporal optimization strategy for scaling the link-level network travel times using the simulated trip data without additional data sources on the network state. The method is tested on the widely used New York City (NYC) Taxi data and shows that the travel times produced using the scaled network are very close to the recorded travel times in the original data. Additionally, the paper studies the performance differences of AMoD simulation when the scaled network is used. The results indicate that realistic travel times can significantly impact AMoD simulation outcomes.

Mobility-on-Demand (MoD) services have been an active research topic in recent years. Many studies focused on developing control algorithms to supply efficient services. To cope with a large search space to solve the underlying vehicle routing problem, studies usually apply hard time-constraints on pick-up and drop-off while considering static network travel times to reduce computational time. As travel times in real street networks are dynamic and stochastic, assigned routes considered feasible by the control algorithm in one time step might become infeasible in the next. Since once assigned and confirmed, customers should still be part of the solution, damage control is necessary to counteract this effect. In this study, a detailed simulation framework for MoD services is coupled with a microscopic traffic simulation to create dynamic and stochastic travel times, and tested in a case study for Munich, Germany. Results showed that the combination of inaccurate travel time estimation and damage control strategies for infeasible routes deteriorates the performance of MoD services -- hailing and pooling -- significantly. Moreover, customers suffer from unreliable pick-up time and travel time estimations. Allowing re-assignments of initial vehicle schedules according to updated system states helps to restore system efficiency and reliability, but only to a minor extent., Comment: Submitted to 26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023

On-Demand Ride-Pooling services have the potential to increase traffic efficiency compared to private vehicle trips by decreasing parking space needed and increasing vehicle occupancy due to higher vehicle utilization and shared trips, respectively. Thereby, an operator controls a fleet of vehicles that serve requested trips on-demand while trips can be shared. In this highly dynamic and stochastic setting, asymmetric spatio-temporal request distributions can drive the system towards an imbalance between demand and supply when vehicles end up in regions with low demand. This imbalance would lead to low fleet utilization and high customer waiting times. This study proposes a novel rebalancing algorithm to predictively reposition idle fleet vehicles to reduce this imbalance. The algorithm first samples artificial requests from a predicted demand distribution and simulates future fleet states to identify supply shortages. An assignment problem is formulated that assigns repositioning trips considering multiple samples and forecast horizons. The algorithm is implemented in an agent-based simulation framework and compared to multiple state-of-the-art rebalancing algorithms. A case study for Chicago, Illinois shows the benefits of applying the repositioning strategy by increasing service rate and vehicle revenue hours by roughly 50% compared to a service without repositioning. It additionally outperforms all comparison algorithms by serving more customers, increasing the pooling efficiency and decreasing customer waiting time regardless of the forecasting method applied. As a trade-off, the computational time increases, but with a termination within a couple of seconds it is still applicable for large-scale real world instances., Comment: Submitted to TRB Annual Meeting 2024

Urban traffic state estimation is pivotal in furnishing precise and reliable insights into traffic flow characteristics, thereby enabling efficient traffic management. Traditional traffic estimation methodologies have predominantly hinged on labor-intensive and costly techniques such as loop detectors and floating car data. Nevertheless, the relentless progression in autonomous driving technology has catalyzed an increasing interest in capitalizing on the extensive potential of on-board sensor data, giving rise to a novel concept known as "Autonomous Vehicles as a Sensor" (AVaaS). This paper innovatively refines the AVaaS concept by simulating the data collection process. We take real-world sensor attributes into account and employ more accurate estimation techniques based on the on-board sensor data. Such data can facilitate the estimation of high-resolution, link-level traffic states and, more extensively, online cluster- and network-level traffic states. We substantiate the viability of the AVaaS concept through a case study conducted using a real-world traffic simulation in Ingolstadt, Germany. The results attest to the ability of AVaaS in estimating both microscopic (link-level) and macroscopic (cluster- and network-level) traffic states, thereby highlighting the immense potential of the AVaaS concept in effecting precise and reliable traffic state estimation and also further applications., Comment: 15 pages, 11 figures, the 2024 TRB Annual Meeting

The Mobilit\"at.Leben study investigated travel behavior effects of a natural experiment in Germany. In response to the 2022 cost-of-living crisis, two policy measures to reduce travel costs for the population in June, July, and August 2022 were introduced: a fuel excise tax cut and almost fare-free public transport with the so-called 9-Euro-Ticket. The announcement of a successor ticket to the 9-Euro-Ticket, the so-called Deutschlandticket, led to the immediate decision to continue the study. The Mobilit\"at.Leben study has two periods, the 9-Euro-Ticket period and the Deutschlandticket period, and comprises two elements: several questionnaires and a smartphone-based passive waypoint tracking. The entire duration of the study was almost thirteen months. In this paper, we report on the study design, the recruitment strategy, the study participation in the survey, and the tracking parts, and we share our experience in conducting such large-scale panel studies. Overall, 3,080 people registered for our study of which 1,420 decided to use the smartphone tracking app. While the relevant questionnaires in both phases have been completed by 818 participants, we have 170 study participants who completed the tracking in both phases and all relevant questionnaires. We find that providing a study compensation increases participation performance. It can be concluded that conducting year-long panel studies is possible, providing rich information on the heterogeneity in travel behavior between and within travelers.

In spring 2022, the German federal government agreed on a set of policy measures that aimed at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These included among others, a nationwide public transport ticket for 9~Euro per month for three months in June, July, and August 2022. In transport policy research this is an almost unprecedented behavioral experiment. It allows us to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of these instruments. We observe this natural experiment with a three-wave survey and a smartphone-based travel diary with passive tracking on an initial sample of 2,261 participants with a focus on the Munich metropolitan region. This area is chosen as it offers a variety of mode options with a dense and far-reaching public transport network that even provides good access to many leisure destinations. The app has been providing data from 756 participants until the end of September, the three-wave survey by 1,402, and the app and the three waves by 637 participants. In this paper, we report on the study design, the recruitment and study participation as well as the impacts of the policy measures on the self-reported and app-observed travel behavior; we present results on consumer choices for a successor ticket to the 9-Euro-Ticket that started in May 2023. We find a substantial shift in the modal share towards public transport from the car in our sample during the 9-Euro-Ticket period in travel distance (around 5 %) and in trip frequency (around 7 %). The mobility outcomes of the 9-Euro-Ticket however provide evidence that cheap public transport as a policy instrument does not suffice to incentive sustainable travel behavior choices and that other policy instruments are required in addition.

In a response to the 2022 cost-of-living crisis in Europe, the German government implemented a three-month fuel excise tax cut and a public transport travel pass for 9 Euro per month valid on all local and regional services. Following this period, a public debate immediately emerged on a successor to the so-called "9-Euro-Ticket", leading to the political decision of introducing a similar ticket priced at 49 Euro per month in May 2023, the so-called "Deutschlandticket". We observe this introduction of the new public transport ticket with a sample of 818 participants using a smartphone-based travel diary with passive tracking and a two-wave survey. The sample comprises 510 remaining participants of our initial "9-Euro-Ticket study from 2022 and 308 participants recruited in March and early April 2023. In this report we report on the status of the panel before the introduction of the "Deutschlandticket".

In spring 2022, the German federal government agreed on a set of measures that aim at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures include among others, a nation-wide public transport ticket for 9 EUR per month and a fuel tax cut that reduces fuel prices by more than 15%. In transportation research this is an almost unprecedented behavioral experiment. It allows to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of transport policy instruments. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample of hundreds of participants as well as an analysis of traffic counts. In this fifth report, we present first analyses of the recorded tracking data. 910 participants completed the tracking until September, 30th. First, an overview over the socio-demographic characteristics of the participants within our tracking sample is given. We observe an adequate representation of female and male participants, a slight over-representation of young participants, and an income distribution similar to the one known from the "Mobilit\"at in Deutschland" survey. Most participants of the tracking study live in Munich, Germany. General transportation statistics are derived from the data for all phases of the natural experiment - prior, during, and after the 9 EUR-Ticket - to assess potential changes in the participants' travel behavior on an aggregated level. A significant impact of the 9 EUR-Ticket on modal shares can be seen. An analysis of the participants' mobility behavior considering trip purposes, age, and income sheds light on how the 9 EUR-Ticket impacts different social groups and activities. We find that age, income, and trip purpose significantly influence the impact of the 9 EUR-Ticket on the observed modal split.

In spring 2022, the German federal government agreed on a set of measures that aimed at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures included among others, a nation-wide public transport ticket for 9EUR per month and a fuel tax cut that reduced fuel prices by more than 15%. In transportation policy and travel behavior research this is an almost unprecedented behavioral experiment. It allows to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of transport policy instruments. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample 2'263 individuals; for the Munich Study, 919 participants in the survey-and-app group and 425 in the survey-only group have been successfully recruited, while 919 participants have been recruited through a professional panel provider to obtain a representative nation-wide reference group for the three-wave survey. In this fourth report we present the results of the third wave. At the end of the study, all three surveys have been completed by 1'484 participants and 642 participants completed all three surveys and used the travel diary throughout the entire study. Based on our results we conclude that when offering a 49EUR-Ticket as a successor to the 9~EUR-Ticket and a local travel pass for 30EUR/month more than 60% of all 9~EUR-Ticket owners would buy one of the two new travel passes. In other words, a substantial increase in travel pass ownership in Germany can be expected, with our modest estimate being around 20%. With the announcement of the introduction of a successor ticket in 2023 as well as with the prevailing high inflation, this study will continue into the year 2023 to monitor the impact on mobility and daily activities., Comment: arXiv admin note: text overlap with arXiv:2208.14902

If private vehicle trips can be replaced, ride-pooling services can decrease parking space needed by higher vehicle utilization and increase traffic efficiency by increasing vehicle occupancy. Nevertheless, substantial benefits can only be achieved if a certain market penetration is passed to find enough shareable rides for pooling to take place. Additionally, because of their highly dynamic and stochastic nature on-demand ride-pooling services cannot always guarantee that a request is served. Allowing customers to pre-book their trip in advance could provide benefits for both aspects. Additional knowledge helps an operator to better plan vehicle schedules to improve service efficiency while an accepted trip or a rejection can be communicated early on to the customer. This study presents a simulation framework where a ride-pooling provider offers a service in mixed operation: Customers can either use the service on-demand or pre-book trips. A graph-based batch optimization formulation is proposed to create offline schedules for pre-booking customers. Using two rolling horizons, this offline solution is forwarded to an online optimization for on-demand and pre-booking customers simultaneously. The framework is tested in a case study for Manhattan, NYC. That the graph-based batch optimization is superior to a basic insertion method in terms of solution quality and run-time. Due to additional knowledge, the ride-pooling operator can improve the solution quality significantly by serving more customers while pooling efficiency can be increased. Additionally, customers have shorter waiting and detour times the more customers book a trip in advance.

In spring 2022, the German federal government agreed on a set of measures that aimed at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures included among others, a nation-wide public transport ticket for 9\ EUR per month and a fuel tax cut that reduced fuel prices by more than 15\,\%. In transportation research this is an almost unprecedented behavioral experiment. It allows to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of transport policy instruments. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample of hundreds of participants as well as an analysis of traffic counts. In this third report, we provide first findings from the second survey, conducted during the experiment., Comment: arXiv admin note: text overlap with arXiv:2206.10510

The market share of mobility on-demand (MoD) services strongly increased in recent years and is expected to rise even higher once vehicle automation is fully available. These services might reduce space consumption in cities as fewer parking spaces are required if private vehicle trips are replaced. If rides are shared additionally, occupancy related traffic efficiency is increased. Simulations help to identify the actual impact of MoD on a traffic system, evaluate new control algorithms for improved service efficiency and develop guidelines for regulatory measures. This paper presents the open-source agent-based simulation framework FleetPy. FleetPy (written in the programming language "Python") is explicitly developed to model MoD services in a high level of detail. It specially focuses on the modeling of interactions of users with operators while its flexibility allows the integration and embedding of multiple operators in the overall transportation system. Its modular structure ensures the transferabillity of previously developed elements and the selection of an appropriate level of modeling detail. This paper compares existing simulation frameworks for MoD services and highlights exclusive features of FleetPy. The upper level simulation flows are presented, followed by required input data for the simulation and the output data FleetPy produces. Additionally, the modules within FleetPy and high-level descriptions of current implementations are provided. Finally, an example showcase for Manhattan, NYC provides insights into the impacts of different modules for simulation flow, fleet optimization, traveler behavior and network representation.

In spring 2022, the German federal government agreed on a set of measures that aim at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures include among others, a nation-wide public transport ticket for 9 EUR per month and a fuel tax cut that reduces fuel prices by more than 15%. In transportation research this is an almost unprecedented behavioral experiment. It allows to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of transport policy instruments. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample of hundreds of participants as well as an analysis of traffic counts. In this second report, we update the information on study participation, provide first insights on the smartphone app usage as well as insights on the first wave results, particularly on the 9 EUR-ticket purchase intention.

In spring 2022, the German federal government agreed on a set of measures that aim at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures include among others, a nation-wide public transport ticket for 9 EUR per month and a fuel tax cut that reduces fuel prices by more than 15% . In transportation research this is an almost unprecedented behavioral experiment. It allows to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of transport policy instruments. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample of hundreds of participants as well as an analysis of traffic counts. In this first report, we inform about the study design, recruiting and initial participation of study participants.

This paper examines the integration of freight delivery into the passenger transport of an on-demand ride-pooling service. The goal of this research is to use existing passenger trips for logistics services and thus reduce additional vehicle kilometers for freight delivery and the total number of vehicles on the road network. This is achieved by merging the need for two separate fleets into a single one by combining the services. To evaluate the potential of such a mobility-on-demand service, this paper uses an agent-based simulation framework and integrates three heuristic parcel assignment strategies into a ride-pooling fleet control algorithm. Two integration scenarios (moderate and full) are set up. While in both scenarios passengers and parcels share rides in one vehicle, in the moderate scenario no stops for parcel pick-up and delivery are allowed during a passenger ride to decrease customer inconvenience. Using real-world demand data for a case study of Munich, Germany, the two integration scenarios together with the three assignment strategies are compared to the status quo, which uses two separate vehicle fleets for passenger and logistics transport. The results indicate that the integration of logistics services into a ride-pooling service is possible and can exploit unused system capacities without deteriorating passenger transport. Depending on the assignment strategies nearly all parcels can be served until a parcel to passenger demand ratio of 1:10 while the overall fleet kilometers can be deceased compared to the status quo.

Autonomous mobility on demand services have the potential to disrupt the future mobility system landscape. Ridepooling services in particular can decrease land consumption and increase transportation efficiency by increasing the average vehicle occupancy. Nevertheless, because ridepooling services require a sufficient user base for pooling to take effect, their performance can suffer if multiple operators offer such a service and must split the demand. This study presents a simulation framework for evaluating the impact of competition and cooperation among multiple ridepooling providers. Two different kinds of interaction via a broker platform are compared with the base cases of a single monopolistic operator and two independent operators with divided demand. In the first, the broker presents trip offers from all operators to customers (similar to a mobility-as-a-service platform), who can then freely choose an operator. In the second, a regulated broker platform can manipulate operator offers with the goal of shifting the customer-operator assignment from a user equilibrium towards a system optimum. To model adoptions of the service design depending on the different interaction scenario, a game setting is introduced. Within alternating turns between operators, operators can adapt parameters of their service (fleet size and objective function) to maximize profit. Results for a case study based on Manhattan taxi data, show that operators generate the highest profit in the broker setting while operating the largest fleet. Additionally, pooling efficiency can nearly be maintained compared to a single operator. With the resulting increased service rate, the regulated competition benefits not only operators (profit) and cities (increased pooling efficiency), but also customers. Contrarily, when users can decide freely, the lowest pooling efficiency and operator profit is observed., Comment: Submitted to Frontiers in Future Transportation

The MobilityCoin is a new, all-encompassing currency for the management of the multimodal urban transportation system. MobilityCoins includes and replaces various existing transport policy instruments while also incentivizing a shift to more sustainable modes as well as empowering the public to vote for infrastructure measures.

This paper studies the joint reconstruction of traffic speeds and travel times by fusing sparse sensor data. Raw speed data from inductive loop detectors and floating cars as well as travel time measurements are combined using different fusion techniques. A novel fusion approach is developed which extends existing speed reconstruction methods to integrate low-resolution travel time data. Several state-of-the-art methods and the novel approach are evaluated on their performance in reconstructing traffic speeds and travel times using various combinations of sensor data. Algorithms and sensor setups are evaluated with real loop detector, floating car and Bluetooth data collected during severe congestion on German freeway A9. Two main aspects are examined: (i) which algorithm provides the most accurate result depending on the used data and (ii) which type of sensor and which combination of sensors yields higher estimation accuracies. Results show that, overall, the novel approach applied to a combination of floating-car data and loop data provides the best speed and travel time accuracy. Furthermore, a fusion of sources improves the reconstruction quality in many, but not all cases. In particular, Bluetooth data only provide a benefit for reconstruction purposes if integrated distinctively., Comment: 20 pages, 9 figures, presented at the 2021 Annual Meeting of the Transportation Research Board (TRB)

This paper presents a dedicated Deep Neural Network (DNN) architecture that reconstructs space-time traffic speeds on freeways given sparse data. The DNN is constructed in such a way, that it learns heterogeneous congestion patterns using a large dataset of sparse speed data, in particular from probe vehicles. Input to the DNN are two equally sized input matrices: one containing raw measurement data, and the other indicates the cells occupied with data. The DNN, comprising multiple stacked convolutional layers with an encoding-decoding structure and feed-forward paths, transforms the input into a full matrix of traffic speeds. The proposed DNN architecture is evaluated with respect to its ability to accurately reconstruct heterogeneous congestion patterns under varying input data sparsity. Therefore, a large set of empirical Floating-Car Data (FCD) collected on German freeway A9 during two months is utilized. In total, 43 congestion distinct scenarios are observed which comprise moving and stationary congestion patterns. A data augmentation technique is applied to generate input-output samples of the data, which makes the DNN shift-invariant as well as capable of managing varying data sparsities. The DNN is trained and subsequently applied to sparse data of an unseen congestion scenario. The results show that the DNN is able to apply learned patterns, and reconstructs moving as well as stationary congested traffic with high accuracy; even given highly sparse input data. Reconstructed speeds are compared qualitatively and quantitatively with the results of several state-of-the-art methods such as the Adaptive Smoothing Method (ASM), the Phase-Based Smoothing Method (PSM) and a standard Convolutional Neural Network (CNN) architecture. As a result, the DNN outperforms the other methods significantly.

Mobility-on-demand (MoD) systems, especially ride-hailing systems, have seen tremendous growth in recent years. These systems provide user-centric mobility services, whose users expect a high level of convenience. Waiting for a response after an app request and eventually learning after a long period of time that no vehicle is available is hardly acceptable. This study investigates the use-case where users should be served within a certain maximum waiting time. Under certain assumptions, which are reasonable for an attractive MoD business model, it can be shown that an operator using dynamic state optimization can communicate a rejection to users after the first iteration, thereby eliminating unnecessary waiting time before these users would leave the system. Furthermore, early operator rejections reduce the dimension of subsequent customer-vehicle assignment problems, thereby saving computational resources and solving the problems faster. In turn, this allows shorter re-optimization cycles and once again faster responses, i.e. a better user experience., Comment: 6 pages, 2 figures, submitted to IEEE ITSC 2021

The goal of this paper is to develop a modeling framework that captures the inter-decision dynamics between mobility service providers (MSPs) and travelers that can be used to optimize and analyze policies/regulations related to MSPs. To meet this goal, the paper proposes a tri-level mathematical programming model with a public-sector decision maker (regulator) at the highest level, the MSP in the middle level, and travelers at the lowest level. The regulator aims to maximize social welfare via implementing regulations, policies, plans, transit service designs, etc. The MSP aims to maximize profit by adjusting its service designs. Travelers aim to maximize utility by changing their modes and routes. The travelers' decisions depend on the regulator and MSP's decisions while the MSP decisions themselves depend on the regulator's decisions. To solve the tri-level mathematical program, the study employs Bayesian optimization (BO) within a simulation-optimization solution approach. At the lowest level, the solution approach includes an agent-based transportation system simulation model to capture travelers' behavior subject to specific decisions made by the regulator and MSP. The agent-based transportation simulation model includes a mode choice model, a road network, a transit network, and an MSP providing automated mobility-on-demand (AMOD) service with shared rides. The modeling and solution approaches are applied to Munich, Germany in order to validate the model. The case study investigates the tolls and parking costs the city administration should set, as well as changes in the public transport budget and a limitation of the AMOD fleet size. Best policy settings are derived for two social welfare definitions, in both of which the AMOD fleet size is not regulated as the shared-ride AMOD service provides significant value to travelers in Munich., Comment: 37 pages, 13 figures, submitted to Transportation Research Part C, Special Issue on ISTTT24

Most traffic state forecast algorithms when applied to urban road networks consider only the links in close proximity to the target location. However, for longer-term forecasts also the traffic state of more distant links or regions of the network are expected to provide valuable information for a data-driven algorithm. This paper studies these expectations of using a network clustering algorithm and one year of Floating Car (FCD) collected by a large fleet of vehicles. First, a clustering algorithm is applied to the data in order to extract congestion-prone regions in the Munich city network. The level of congestion inside these clusters is analyzed with the help of statistical tools. Clear spatio-temporal congestion patterns and correlations between the clustered regions are identified. These correlations are integrated into a K- Nearest Neighbors (KNN) travel time prediction algorithm. In a comparison with other approaches, this method achieves the best results. The statistical results and the performance of the KNN predictor indicate that the consideration of the network-wide traffic is a valuable feature for predictors and a promising way to develop more accurate algorithms in the future., Comment: Presented at Annual Meeting of the Transport Research Board (TRB), 2019

A recently proposed paradigm for vehicular traffic in the era of CAV (connected and automated vehicles), called TrafficFluid, involves lane-free vehicle movement. Lane-free traffic implies that incremental road widening (narrowing) leads to corresponding incremental increase (decrease) of capacity; and this opens the way for consideration of real-time internal boundary control on highways and arterials, in order to flexibly share the total (both directions) road width and capacity among the two directions in dependence of the bi-directional demand and traffic conditions, so as to maximize the total (two directions) flow efficiency. The problem is formulated as a convex QP (Quadratic Programming) problem that may be solved efficiently, and representative case studies shed light on and demonstrate the features, capabilities and potential of the novel control action.

With ongoing developments in digitalization and advances in the field of autonomous driving, on-demand ride pooling is a mobility service with the potential to disrupt the urban mobility market. Nevertheless, to apply this kind of service successfully efficient algorithms have to be implemented for effective fleet management to exploit the benefits associated with this mobility service. Especially real time computation of finding beneficial assignments is a problem not solved for large problem sizes until today. In this study, we show the importance of using advanced algorithms by comparing a fast, but simple insertion heuristic algorithm with a state-of-the-art multi-step matching algorithm. We test the algorithms in various scenarios based on private vehicle trip OD-data for Munich, Germany. Results indicate that in the tested scenarios by using the multi-step algorithm up to 8$\%$ additional requests could be served while also 10$\%$ additional driven distance could be saved. However, computational time for finding optimal assignments in the advanced algorithm exceeds real time rather fast as problem size increases. Therefore, several aspects to reduce the computational time by decreasing redundant checks of the advanced multi step algorithm are introduced. Finally, a refined vehicle selection heuristic based on three rules is presented to furthermore reduce the computational effort. In the tested scenarios this heuristic can speed up the most cost intensive algorithm step by a factor of over 8, while keeping the number of served requests almost constant and maintaining around 70$\%$ of the driven distance saved in the system. Considering all algorithm steps, an overall speed up of 2.5 could be achieved., Comment: Presented at Informs Annual Meeting 2019 11 pages 5 figures