added TR-A andres

_data/publications.json

@@ -1,4 +1,27 @@
 [
+    {
+        "title": "Improving public transportation via line-based integration of on-demand ridepooling",
+        "authors": [
+            "Andres Fielbaum",
+            "Alejandro Tirachini",
+            "Javier Alonso-Mora"
+        ],
+        "date": "2024-12-01",
+        "type": "journal",
+        "venue": "Transportation Research Part A: Policy and Practice",
+        "links": [
+            {
+                "pdf": "/assets/files/publications/24-fielbaum-tra"
+            }
+        ],
+        "belongs_to_projects": [
+            "ridepooling"
+        ],
+        "topics":[
+            "task planning", "transportation"
+        ],
+        "abstract": "Local motion planning is a heavily researched topic in the field of robotics with many promising algorithms being published every year. However, it is difficult and time- consuming to compare different methods in the field. In this paper, we present localPlannerBench, a new benchmarking suite that allows quick and seamless comparison between local motion planning algorithms. The key focus of the project lies in the extensibility of the environment and the simulation cases. Out-of-the-box, localPlannerBench already supports many simulation cases ranging from a simple 2D point mass to full-fledged 3D 7DoF manipulators, and it’s straightforward to add your own custom robot using a URDF file. A post-processor is built-in that can be extended with custom metrics and plots. To integrate your own motion planner, simply create a wrapper that derives from the provided base class. Ultimately we aim to improve the reproducibility of local motion planning algorithms and encourage standardized open-source comparison."
+      }, 
     {
         "title": "Local Planner Bench: Benchmarking for Local Motion Planning",
         "authors": [
@@ -20,7 +43,7 @@
         "topics":[
             "motion planning"
         ],
-        "abstract": "Local motion planning is a heavily researched topic in the field of robotics with many promising algorithms being published every year. However, it is difficult and time- consuming to compare different methods in the field. In this paper, we present localPlannerBench, a new benchmarking suite that allows quick and seamless comparison between local motion planning algorithms. The key focus of the project lies in the extensibility of the environment and the simulation cases. Out-of-the-box, localPlannerBench already supports many simulation cases ranging from a simple 2D point mass to full-fledged 3D 7DoF manipulators, and it’s straightforward to add your own custom robot using a URDF file. A post-processor is built-in that can be extended with custom metrics and plots. To integrate your own motion planner, simply create a wrapper that derives from the provided base class. Ultimately we aim to improve the reproducibility of local motion planning algorithms and encourage standardized open-source comparison."
+        "abstract": "Ride-sourcing companies have worsened congestion in numerous cities worldwide, as many users are attracted from more sustainable modes. To reverse this trend, it is crucial to leverage the technology of connecting users and vehicles online and use it to strengthen public transport, which can be achieved by integrating on-demand pooled services with existing fixed-line services. We propose an efficient and practical integration idea: namely, to complement fixed bus lines with a fleet of smaller vehicles that follow flexible (on-demand) routes side-by-side with the fixed routes, so that part of the demand that would have used the fixed line can ride the flexible service instead. With this scheme, a smaller bus fleet is required, partially compensating for the increase in operators’ costs stemming from the flexible vehicles. This integration strategy favors mostly two types of users: those traveling in low-demand periods, through lower waiting times, and those located far from the bus stops, because the on-demand vehicles can reduce their access time. We develop simulations in real-world scenarios from Santiago, Chile, and Berlin, Germany, for the cases of human-driven and automated vehicles. Results show that when vehicles are automated: (i) A small number of on-demand vehicles can reduce average walking times from approximately 12 to 2 min while reducing operators’ costs, leading to a Pareto improvement, (ii) A larger number of on-demand vehicles can diminish total costs by 13%–39%, through a reduction in users’ costs, although increasing operators’ costs. If vehicles are not automated, total costs are reduced by more than 10% in all of the scenarios analyzed, but a Pareto improvement is not always possible. In general, this mixed fixed/on-demand system outperforms the use of on-demand ridepooling only. Results are more promising in Berlin, because large buses are cheaper in Santiago and run more crowded, so it is more costly to partially replace them by smaller vehicles."
       }, 
     {
         "title": "Topology-Driven Parallel Trajectory Optimization in Dynamic Environments",